Vol. XXVIII Issue 2 / ARTICLE 4 - Journal of Basic & Applied Genetics

Vol. XXVIII Issue 2
Article 4

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1" /><title>Untitled Document</title></head><body>ARTÍCULOS ORIGINALESMigrations, admixture and genetic diversity in Central  Argentinian Patagonia: analysis of autosomal Alu  polymorphismsMigraciones, mestizaje y diversidad genética en la Patagonia  Central Argentina: análisis de polimorfismos autosómicos Alu Parolin M.L.1*, Zanetti D.2, Calò C.M.3, Esteban E.2, Avena S.4, Carnese F.R.4, Moral P.21 Instituto de Diversidad y Evolución Austral, CCT CENPAT-CONICET, Puerto Madryn, Argentina.  2 Departament Biologia Animal, Unitat d’Antropologia, Facultat de Biologia, Universitat de Barcelona, Spain.  3 Dipartimento di Scienze della vita e dell’ ambiente, Università de Cagliari, Sardegna Italia.  4 Universidad Maimónides e Instituto de Ciencias Antropológicas, Facultad de Filosofía y Letras, Universidad de Buenos Aires, Argentina.  * corresponding author: <a href="mailto:parolin@cenpat-conicet.gob.ar">parolin@cenpat-conicet.gob.ar</a>Fecha de recepción: 20/02/2017  Fecha de aceptación de versión final: 25/10/2017<hr />ABSTRACTThis study aimed to analyze autosomal Alu insertions in three localities from Patagonia Argentina belonging to the Andes region and the coast of the Chubut province. Knowledge of the genetic diversity of these populations, along with the genealogical data, will contribute to better understand historical information, differential migration process and bio-demographic composition of the Central Patagonia region. In order to achieve this objective, 16 autosomal Alu insertion polymorphisms were genotyped: ACE, APO-A1, TPA25, FXIIIB, A25, HS4.32, D1, HS4.69, HS2.43, Sb19.12, Yb8NBC120, Sb19.3, Yb8NBC125, Ya5NBC221, DM, and CD4. Our results showed that the Central Patagonia region presents a complex continental genetic admixture with marked Native American roots (39% ± 1.2), Eurasian (56% ± 1.73) and, to a lesser extent, African (5% ± 1.7). The genetic proximity of the Patagonian samples in relation to groups from Europe and Northern Africa, but with a displacement towards the native communities, constitutes a clear indicator of the differential admixture process that took place in different regions of Argentina. Moreover, genetic differences were observed between Patagonian localities and Bahía Blanca (Central region of Argentina). These observations warned us that population genetic constitution analysis cannot be approached without bearing in mind the regional particularities, which are the result of the different historical, migratory, social-economic and demographic processes that occurs in the country.Key words: Alu insertion polymorphisms; Argentina Central Patagonia; Admixture; Migrations.RESUMENEste estudio tiene como objetivo el análisis de las inserciones autosómicas Alu en tres localidades de la Patagonia argentina localizadas en la región andina y costera de la provincia de Chubut. El conocimiento de la diversidad genética de estas poblaciones, junto con los datos genealógicos, contribuirán a una mejor comprensión de la información histórica, los procesos migratorios diferenciales y la composición bio-demográfica de la región central Patagónica. Para alcanzar este objetivo se analizaron 16 polimorfismos autosómicos de inserción Alu: ACE, APO-A1, TPA25, FXIIIB, A25, HS4.32, D1, HS4.69, HS2.43, Sb19.12, Yb8NBC120, Sb19.3, Yb8NBC125, Ya5NBC221, DM y CD4. Nuestros resultados mostraron que la región central Patagónica presenta una mezcla genética continental compleja de marcadas raíces nativo americanas 39% (± 1.2), eurasiáticas 56% (± 1.73) y, en menor medida, africanas 5% (± 1.7). La proximidad genética de las muestras patagónicas a los grupos de Europa y del Norte de África, pero con un mayor desplazamiento hacia las comunidades nativas, constituye un claro indicador del proceso de mezcla diferencial que tuvo lugar en las distintas regiones de la Argentina. Por otra parte, las diferencias genéticas observadas entre las localidades de Patagonia y Bahía Blanca (región central de la Argentina), nos advierten que no puede analizarse la constitución genética de las poblaciones sin tener en cuenta las particularidades regionales, que son el resultado de los diferentes procesos históricos, migratorios, socio-económicos y demográficos que ocurrieron en el interior del país.Palabras clave: Polimorfismos de inserción Alu; Patagonia Central Argentina; Mestizaje; Migraciones.<hr /> INTRODUCTIONArgentina has a history of widespread admixture among Native American, Eurasian and African groups, with particular characteristics in each region of the country (Toscanini et al., 2007; Avena et al., 2012; Parolin et al., 2013a, 2015a; García et al., 2015). Knowledge of the genetic composition of urban populations allows the reconstruction of the biological identity of their inhabitants, both at local and regional levels. At the same time, this information can be useful to understand the admixture events associated with different foundational histories, migrations and demographic changes that characterized the populations under study.  Previous studies carried out in the Central Patagonia region by means of blood systems analysis, showed an Eurasian contribution of 51%, a Native American contribution of 47%, and an African contribution of 2% in the Andean locality of Esquel (ESQ) (Avena et al., 2010). The corresponding ancestral contributions were 59%, 37% and 4% respectively in Comodoro Rivadavia (CR) (Avena et al., 2009) and 67%, 29% and 4% in Puerto Madryn (PM) (Parolin et al., 2013b). More recently, by means of the analysis of 99 ancestry informative autosomal markers (AIMs) in a combination of samples from ESQ and CR, the above genetic composition was estimated to be 52% Eurasian, 44% Native American, and 4% African (Avena et al., 2012). Likewise, through the study of 22 STR autosomal markers in samples from filiation cases from Chubut province, ancestral contributions were estimated as 50% European, 46% Native American and 4% African (Parolin et al., 2015b). Uniparental genetic data obtained in Central Patagonia (Avena et al., 2009, 2010; Parolin et al., 2013b, 2014), supports the idea that in Argentina the native maternal participation rises towards the North (83-94%) and South of the country, including Patagonia (53-78%), and decreases to an average of 45% in the Central region. Meanwhile, the native paternal contribution is observed at a mean of 6% in every urban population studied by our research team, except for Northwest Argentina (NWA), with 17.3% (Di Fabio Roca et al., 2016) and the Andean localities of ESQ, with 23% (Parolin et al., 2013a), and San Carlos de Bariloche (SCB), with 18% (Parolin et al., 2015a). Other authors have observed this same trend upon studying urban and rural areas from the center and North of Argentina (Martínez Marignac et al., 2004; Wang et al., 2008; García and Demarchi, 2009; Bailliet et al., 2011; Motti et al., 2013). There is also a strong correlation between genetic data and the donors´ genealogical information, and between genetic data and the historical and demographic data of each locality. In this regard, it has been observed that genetic contribution of foreign migrants (mainly from bordering countries) to the relatively small receptive populations, increases towards the South of the country. At local level, ESQ presents the largest proportion of grandparents and donors born in the same city and its area of influence, whereas TW and PM exhibit a larger number of internal migrants from other Argentinean regions, mainly from the center of the country, with largerEuropean ancestry (Parolin et al., 2013b). However, we point out the existence of an important Bolivian community inhabiting the lower valley of the Chubut River region (Rossini et al., 2007). In addition, TW, CR and SCB present a strong influence of migrations from bordering countries, particularly of Chilean origin, with strong Native American ancestry (Bandieri, 2001; INDEC: Censo Nacional de Población, Hogares y Viviendas 2010- http//indec.mecon.ar). Moreover, CR is the Patagonian city with the highest migratory flow from NWA, attracted by the large oil industry that characterizes the city. These differences observed from previous studies carried out in the interior of the same region and even in the same province, as it is the case of Chubut, cautioned us about the fact that the analysis of the genetic composition of the populations must take into consideration particularities of local and regional historical migrations.  Alu polymorphisms are repetitive DNA insertions of 300-1,000 bp which are spread along the human genome and constitute approximately 5% out of its total sequence. Their origin is estimated at around 65 million years BP and they are found exclusively in primates. These markers are especially useful in evolutionary studies because of a number of characteristics that they share: high stability, neutrality, known ancestral state and low propensity to exhibit homoplasy (Batzer and Deininger, 2002; Rishishwar et al., 2015). These properties make the analysis of Alu insertions a tool of great utility for the analysis of biological relations among populations, reconstruction of historical processes, and population structures. Moreover, they have been successfully used as ancestry markers to estimate the geographic descent of the human populations (Luizon et al., 2007).  Alu markers have been used to study Eurasian and African populations (Calò et al., 2005; González-Pérez et al., 2003, 2010; Hernando Herráez, 2010; Zanetti et al., 2014) and, in a lesser extent, Native American groups (Battilana et al., 2002, 2006; Dornelles et al., 2004; Mateus- Pereira et al., 2005; Gayà-Vidal et al., 2010; Gómez-Pérez et al., 2013). However, there are only three studies that make use of the Alu insertions as a tool for genetic-population analysis in Argentina: Martínez Marignac et al. (2004) analyzed two Alu markes and three autosomal SNPs to estimate continental admixture (European, African and native American ancestry) in a sample of La Plata city (Buenos Aires province), Resano et al. (2007) analyzed 19 Alu markers to estimate admixture fractions in a sample from Bahía Blanca city (Buenos Aires province), and Gómez-Pérez et al. (2011) analyzed eight autosomal Alu markers to characterize genetic diversity and population structure of the Jujuy province. Motivated by the demonstrated utility of the Alu insertions in population genetic studies, we analyzed 16 autosomal Alu insertion polymorphisms (ACE, APO-A1, TPA25, FXIIIB, A25, HS4.32, D1, HS4.69, HS2.43, Sb19.12, Yb8NBC120, Sb19.3, Yb8NBC125, Ya5NBC221, DM, and CD4) in three localities of Chubut province, Central Patagonia: one in the Andes region (Esquel) and the other two in the coast (Comodoro Rivadavia and Puerto Madryn). Knowledge of the genetic composition of these populations will contribute to understand differential migration and admixture processes in different region of Argentina. At the same time, the knowledge of the genetic structure of a particular population could be a useful tool in biomedical research and forensic sciences.MATERIALS AND METHODSThe populations  DNA of 150 non-related individuals from the cities ofEsquel (ESQ; N= 50), Comodoro Rivadavia (CR; N=50) and Puerto Madryn (PM; N= 50) were analyzed.ESQ is situated in the Andes region, whereas the othertwo cities are in the coastal area of the Chubut province,Central Patagonia, Argentina (<a href="#fig1">Figure 1</a>). Blood sampleswere taken, with prior signed informed consent, fromvolunteer donors who attended the hemotherapy servicesat public and private hospitals in each locality and signed aprior informed consent. The participants were thoroughlyinformed of the scopes of the study and answered aquestionnaire to obtain information about their birthplaceand that of the two preceding generations (parents andgrandparents). Five ml of whole blood were obtained fromeach donor and collected in vacutainer tubes with EDTAanticoagulant (Ethylene Diamine Tetra Acetic Acid).DNA was extracted by the phenol-chloroform method(Sambrook et al., 1989). The protocols and procedures employed in this study were reviewed and approved by bioethics and educational committees of the Regional Hospital of CR, Sub-zonal Hospital of ESQ, and Zonal Hospital of PM.<a name="fig1" id="fig1"></a><img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig1.jpg" width="290" height="368" /> Figure 1. Geographical location of the studied populations.Genotyping  Sixteen Alu insertion polymorphisms (FXIII, HS2.43, D1,HS4.69, A25, TPA25, APOA1, CD4, HS4.32, ACE, Sb19.3,Sb19.12, DM, Yb8NBC120, YbNBC125 and Ya5NBC221),distributed in nine autosomal chromosomes (Nº 1, 3, 6, 8, 11, 12, 17, 19, 22; <a href="#tab1">Table 1</a>), were genotyped. Genotypes were scored by PCR amplification and subsequent electrophoretic migration in agarose gels. Primer sequences and amplification conditions were as previously described (Batzer and Deininger, 1991; Stoneking et al., 1997; González-Pérez et al., 2010). Each PCR reaction included positive and negative amplification controls. Genotype assignments were made by direct observation of 2% agarose gels (CD4, DM and FXIIIB, at 1%) dyed with GelRed 0.5x under UV light.<a name="tab1" id="tab1"></a>Table 1. Amplification characteristics of Alu polymorphisms.  <img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04tab1.jpg" width="573" height="568" />  Ta: Annealing temperature; Chr: chromosome location; PCR product size of Alu-containing/Alu-lacking allele; pb: pairs of bases.Statistical Analysis  Analysis of allelic frequencies, heterozygosity indexes, Hardy- Weinberg equilibrium, differences between population pairs (Fisher’s exact test), molecular analysis of variance (AMOVA) and the respective genetic distances (Reynolds et al., 1983) were performed by using the Genetix v.4.05 software (Belkhir et al., 2002) and Arlequin v3.1 (Excoffier and Lischer, 2010). Admixture estimations, based on the Eurasian-Native American-sub-Saharan trihybrid model, were performed using the ADMIX95 software (http://genetica.fmed.edu.uy/software.htm) which is based on the Chakraborty (1975) genetic identity method. Populations genetic affinities were analyzed by the construction of a principal components graph (PCA), based on Reynolds distances, by using the Rv3.1.1 FactoMineR (Josse, 2008) statistical package. The spatial genetic patterns were estimated through a geographically referenced spatial principal component analysis (sPCA). This method provides evidences of global or local genetic structures by considering Esquel (ESQ) 42°54’ S, 71°19’ O; Comodoro Rivadavia (CR): 45°52’ S, 67°30’ O, and Puerto Madryn (PM): 42°49’ S, 65°4’ O. The Patagonia region is shaded in gray. the geographic distances as well. Global patterns correspond to positive proper values, whereas negative proper values indicate local patterns that indicate considerable genetic differences among neighbors. The importance of global and local patterns was assessed by the Monte-Carlo test with 10,000 permutations, by using the R adegenet package (Jombart et al., 2008).Comparisons with other world populations  In order to analyze the genetic structure of the studiedpopulations and the inter-population relationship, the resultswere compared with those previously obtained in Bahía Blanca and in twelve worldwide populations by other authors (<a href="#fig2">Figure 2</a>). These analyses were carried out using data from 10 Alu insertion polymorphisms shared in the sixteen populations studied (ACE, HS4.69, APOA1, CD4, HS4.32, Sb19.3, Sb19.12, Yb8NBC120, YbNBC125 and Ya5NBC221).<a name="fig2" id="fig2"></a><img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig2.jpg" width="395" height="399" />  Figure 2. Thirteen worldwide populations taken from the literature for comparative purposes. 1-BB: Bahía Blanca (Resano et al., 2007); 2-QUE: Quechua and 3-AYM: Aymara (Gayà-Vidal et al., 2010); 4-C.SP: Central Spain, 5-S.SP: Southern Spain, 6-BAL: Balearic Islands, 7-FRA: France, 8-SIC: Sicily, 9-GRE: Greece, 10-TUR: Turkey, 11-MOR: Morocco, 12-ALG:Algeria and 13-IC: Ivory Coast (González-Pérez et al., 2010).RESULTSThe allelic frequencies of 16 Alu insertions estimated in three Central Patagonia populations are detailed in <a href="#tab2">Table 2</a>. The highest insertion frequencies were recorded for the APOA1 locus in CR (0.98) and for the Ya5NBC221 locus in ESQ (0.94). Towest frequencies were observed for the HS2.43 locus in the three studied populations (0.03 in CR to 0.07 in PM). Moreover, the lowest genetic diversity was recorded for the APOA1 locus in CR (H= 0.04) and the highest heterozygosity for the TPA25 locus (H= 0.62) in PM which, as expected, showed an intermediate value of insertion frequency (0.55). We performed 48 tests (16 loci analyzed/population; p-value=0.001). <a name="tab2" id="tab2"></a>Table 2. Alu insertion frequencies, heterozygosity and Hardy-Weinberg equilibrium in three localities of Central Patagonia (Exact test).  <img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04tab2.jpg" width="570" height="610" /> H: average heterozygosity; p-HW: Hardy-Weinberg equilibrium significance, Bonferroni correction (p-value 0.05/48= 0.001).After Bonferroni correction, , we could not reject the Hardy-Weinberg equilibrium hypothesis for any of the sixteen Alu markers (nominal p>0.001). The AMOVA analysis indicated that most of the total genetic variation in the three studied populations was due to within population differences, whereas only 0.46% of the total genetic variation was due to differences among populations. The matrix of genetic distances based on Reynolds method (Reynolds et al., 1983) did not exhibit significant differences (FST p-values>0.05) among the studied Central Patagonia populations: CR vs. ESQ (FST= 0.0027; p-value= 0.56), CR vs. PM (FST= 0.0051; p-value 0.39), PM vs. ESQ (FST= 0.0102; p-value 0.32). The studied populations were compared with thirteen worldwide populations, arranged in five groups according to geographic-population criteria: Europe, Sub-Saharan Africa, North Africa, Admixed America and Native American (<a href="#fig2">Figure 2</a>). The hierarchic AMOVA analysis was significant (p-value<0.0001) with values of FST= 0.13 (difference among populations), FSC= 0.02 (differences among populations and within groups), and FCT= 0.11 (differences among population groups). These values might indicate the existence of a geographic structure in the analyzed populations. Such geographic structure was studied in more detail with an sPCA analysis. The values of the tests were, respectively, non significant for the global (p-value= 0.27), and significant for the local (p-value= 0.02). This differentiation found at local level might be due to the distinction between Northern Africa and sub- Saharan groups, since the test was not significant when the sample from the Ivory Coast (IC) was removed. The genetic distance matrix obtained with Reynolds et al. (1983) index exhibited significant differences (p-fst<0.05) between the populations of the present study and those from the rest of the world. However, relatively low genetic distances were observed among the Central Patagonia and Bahía Blanca populations (variation range FST= 0.006-0.013), and between populations from Southern and Central Spain (variation range= 0.033- 0.048), whereas larger genetic distances were observed between the Patagonian and Native American populations (variation range= 0.094-0.122). On the other hand, the highest distances were observed between the Patagonian and Sub-Saharan (IC) populations (variation range= 0.192- 0.219), highlighting the outlier position of the latter. The graphic representation of the distances matrix first shows a cluster among the Central Patagonia samples and then with Southern Buenos Aires (BB), with an intermediate position among the Native American, Northern African and European groups (<a href="#fig3">Figure 3A</a>). The first two axes represented 73.4% of the total genetic variance. The first component, including 46.9% of the total variance, clustered the Argentinian populations in a position intermediate between the two Native Bolivian populations and the European and Northern African populations. The second component (26.4%) allowed the distinction of the sub- Saharan African population from the rest of the European, Northern African and South American populations. When the analysis was repeated to eliminate the effect of the Ivory Coast sample, the observed pattern was essentially the same (<a href="#fig3">Figure 3B</a>).<a name="fig3" id="fig3"></a><img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig3.jpg" width="493" height="275" />  Figure 3A. Graphic representation of the genetic distances matrix based on Reynold et al. (1983) distances. Abbreviations of the analyzed populations are described in Figure 2.<img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig3b.jpg" width="506" height="291" />  Figure 3B. Graphic representation of the genetic distance matrix based on Reynold et al. (1983) distances excluding the South African sample (IC: Ivory Coast). Abbreviations of the analyzed populations are described in Figure 2.Based on the trihybrid admixture model of the Latin American populations, the allelic frequencies of 10 Alu loci from Europeans (average C.SP, S.SP, and SIC), Africans (IC) and native Americans (average QUE and AYM) were used to represent the parental populations; the respective references are described in Figure 2. The total Central Patagonia sample had an Eurasian contribution of 56% (± 1.7%), African contribution of 5% (± 1.7%), and Native American contribution of 39% (± 1.2%). At the local level, those contributions were respectively 58% Eurasian (± 2.4%), 1.5% African (± 2.2%), and 40.5% Native American (± 2.0%) in CR city. In ESQ, the contributions were 54.4% (± 5.5%) Eurasian, 3.3% (± 1.2%) African and 42.3% (± 3.6%) Native. Finally, in PM, those values were 56% (± 1.2%) Eurasian, 5% (± 4.8%) African, and 39% (± 1.0%) Native American. Genealogical information obtained for each sample demonstrates local differences in the relationship between the place of birth and geographic origin of the grandparents. In this sense, <a href="#fig4">Figure 4</a> shows that the origin of the grandparents is mainly local in ESQ, from the Central region in PM, and from Chile in CR, whereas the origin ranking in the second place is Chile in PM, and Europe in CR and PM. In a lesser extent, other South American grandparents came from Bolivia, Paraguay, Uruguay, and Brazil.<a name="fig4" id="fig4"></a><img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig4.jpg" width="456" height="223" /> Figure 4. Birthplace of grandparents.DISCUSSION This research constitutes the first genetic characterization of urban populations in the Argentinian Patagonia by means of analysis of Alu insertion polymorphisms. The Argentininian population presents a widespread multiple continental admixture with Native American, Eurasian and, to a lesser extent, African roots. In this sense, the Central Patagonian region is not an exception, as it has been shown previously in studies of uniparental and biparental genetic heritage markers (Avena et al., 2009, 2010, 2012; Parolin et al., 2013a,b, 2014, 2015a,b). In order to complement the previously mentionedstudies, 16 Alu markers were analyzed. The insertion frequencies and the average heterozygosity values were similar to those previously recorded in the Argentinian locality of Bahía Blanca (Resano et al., 2007) and, in general, in Northern Africa and Spain populations (González-Perez et al., 2010). Nevertheless, the CD4 locus presented a higher insertion frequency and a significantly lower heterozygosity value than in previous studies. In this regard, it has been observed that the insertion frequency of CD4 is close to 1.00 in native groups from South America (Gayà-Vidal et al., 2010). The comparatively high frequency of the ancestral allele CD4 might be related to the autoctonous contribution in the analyzed populations. Regarding the genetic differentiation observed at local level, the variation observed in the analyzed Alu markers only presents local discontinuities in the African continent,while these markers are not indicators of significant geneticheterogeneities in the other continents.  The admixture estimation based on the trihybrid modelshowed that the Central Patagonian sample registered anautoctonous contribution (36.5% ± 1.9%) higher thanthe one previously obtained for Southern Buenos Airesprovince (20.9% ± 4.8%). This fact might be related to thelate incorporation of Patagonia to the country (Law 1532,1884), which allowed the native populations to keep theirautonomy for a longer period than other native groups.It could also be related to the constant migratory flowthat the Patagonian region receives from inland provincesand from bordering countries, mainly from Chile, whosepopulations has a large native ancestry (Bandieri, 2001;Eyheramendy et al., 2015; Gómez-Carballa et al., 2016).Nonetheless, possible variations due to chance related tosample size and to the type and quantity of the analyzedgenetic markers cannot be discarded.Likewise, the admixture results obtained at the locallevel are related to the geographic origin of the ancestorsand to the foundational history of each analyzedpopulation. This is reflected in the genealogical data thatshow clear differences in the geographical origin of themigrant grandparents by locality. In this sense, it wasobserved that, in ESQ, there is a higher frequency of birthin which the grandparents were also locally born. Thisfact could be related to a slow growth of the region and abetter preservation of the autoctonous origin (Finkelsteinand Novella, 2006). Since before its foundation (1906) andto present days, ESQ has maintained an intense trade withChile, being a zone of transport and gateway of migrantsin both sides of the Andes mountain range. ESQ has hada gradual and diversified growth, also having agroforestryand tourism as its most important economic activities(Avena et al., 2010). CR has had significant contributionsof grandparents from Chile and northern provinces ofArgentina. Both immigrant groups, which have large nativeancestry (Avena et al., 2012; Bandieri, 2001; Eyheramendyet al., 2015; Gómez-Carballa et al., 2016; Gómez-Pérez et al., 2011; Ramallo et al., 2011), participated in strongmigratory waves attracted by the exponential growth ofthe oil industry, which started in 1907 and is still ongoing(Avena et al., 2009). On the other hand, PM presents the higher proportionof grandparents from the central region of the country(30%), who havemostly European ancestry (Avena et al.,2012), and 23% grandparents born in that continent. Thisfact is related to the migration of qualified laborers for thealuminum industry, mainly from the inner provinces of thecountry, and also to the foundational history of PM, withWelsh, Spanish and Italian roots. Nonetheless, we mustindicate that, during the last two decades, an importantmigration from Boliviahas taken place, representing themigrants from that country 36% of the total comingfrom Latin America (INDEC: www.indec.mecon.ar). TheBolivian community harbors a considerable fraction ofautoctonous ancestry (Heinz et al., 2013) and, nowadays,it is estimated that they represent 5.6% of the SouthAmerican migrants in the Patagonian region (Castillo andGurrieri, 2012). The three cities included in then presentstudy have less than 150 years of existence; thereforethe migrations have been very important in the growthof their populations. ESQ is located in a much morehumid area than the other two cities, at the foot of Andesmountain range. PM and CR are located inthe coast, ina much drier area. The density of the native populationwas much higher in the mountain range than in the coast;this also explains that the native contribution has a localorigin, being high the correspondence with the recordedgenealogical information. Thus, although the percentageof Native American contribution was similar in the threecities, the source of that contribution is different. The genetic affinities among the populations based onthe Reynolds et al. (1983) distances and their graphicalrepresentation in the correspondent PCA show agenetic proximity between the studied samples and theBahía Blanca population. This may be due in part to thegeographic nexus between Patagonia and Buenos Airesprovince, which favors a migratory flow between bothregions. In a similar way, the genetic proximity of theCentral Patagonian samples and the Northern African andEuropean groups, but with a shift towards the autoctonouscommunities, constitutes a clear indicator of the characterof the differential admixture process that took place in thedifferent regions of Argentina. Even though no significantdifferences have been observed among the populations ofthe Central Patagonia region, we would like to highlightthe importance of having genealogical data to betterunderstand and contextualize the genetic data. Thisinformation together with the historical and demographicdata caution us that the analysis of the genetic makeupof the populations should be approached bearing in mindthe local and regional particularities, which are the result of different historical, migratory, and social andeconomic processes. For example, this information helps us to understand that the main source of the autoctonous contribution was local in ESQ, Chilean in CR and of various origins in PM. Finally, our results highlight the usefulness of Alu markers as a tool for the study of the biological relations and the analysis of genetic population admixture. Our results are also in agreement with the Eurasian, African and Native American contributions previously observed in the same populations for blood group systems (Avena et al., 2009, 2010; Parolin et al., 2013b), or using STRs (Parolin et al., 2015b) and in 99 individual ancestry informative markers (Avena et al., 2012) (<a href="#fig5">Figure 5</a>). In conclusion, this study reveals the utility of Alu insertion polymorphisms as a complementary tool for population genetic analysis, being a simple and economical molecular technique. Also, this study shows the need to increase biparental genetic information for American urban and rural populations, which will contribute to better understand the complex migratory and heterogeneous admixture processesobserved in Latin American populations.<a name="fig5" id="fig5"></a><img src="https://sag.org.ar/jbag/wp-content/uploads/2019/11/xxviii2a04fig5.jpg" width="567" height="148" /> Figure 5. Percentage of the estimated genetic admixture in the Central Patagonia region for four autosomal systems.BIBLIOGRAPHY1. Avena S.A., Parolin M.L., Boquet M., Dejean C.B., Postillone M.B., Alvarez Trentini Y., Di Fabio Rocca F., Mansilla F., Jones L., Dugoujon J.M., Carnese F.R. (2010) Mezcla génica y linajes uniparentales en Esquel (Prov. de Chubut). Su comparación con otras muestras poblacionales Argentinas. J. Basic Appl. Genet. 21: 01-14. 2. Avena S.A., Parolin M.L., Dejean C.B., Ríos Part M.C., Fabrikant G., Goicoechea A.S., Dugoujon J.M., Carnese F.R. 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