Testing for genetic structure in different urban Argentinian populations

Forensic Science International 165 (2007) 35–40 www.elsevier.com/locate/forsciint

Testing for genetic structure in different urban Argentinian populations Ulises Toscanini a, Leonor Gusma˜o b, Gabriela Berardi a, Anto´nio Amorim c, ´ ngel Carracedo d, Antonio Salas d, Eduardo Raimondi a,* A a

´ N FAVALORO, Av. Belgrano 1782, 1er Subsuelo, (1093) Capital Federal, Buenos Aires, Argentina PRICAI-FUNDACIO b IPATIMUP, Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Portugal c IPATIMUP, Instituto de Patologia e Imunologia, Molecular da Universidade do Porto, Portugal, Faculdade de Cieˆncias, Universidade do Porto, Portugal d Unidad de Gene´tica, Instituto de Medicina Legal, Facultad de Medicina, Universidad de Santiago de Compostela, A Corun˜a, Galicia, Spain Received 23 December 2005; received in revised form 14 February 2006; accepted 14 February 2006 Available online 17 July 2006

Abstract Fifteen autosomal short tandem repeat (STR) markers (D3S1358, HUMTH01, D21S11, D18S51, PENTA E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA D, HUMvWA, D8S1179, HUMTPOX, FGA) were analyzed in 1734 individuals living in urban areas of cities from six different Argentinian provinces (Buenos Aires, Neuque´n, Tucuma´n, La Pampa, San Luis, Santa Cruz) in order to determine if a common urban database could be used in Argentina for forensic purposes. Frequencies estimates, Hardy–Weinberg equilibrium (HWE), and other parameters of forensic interest were computed. Comparisons between the six populations, and with published data from one Native American population from Argentina and other urban populations from Argentina and Europe were also performed. Our results reveal evidences for population structure, both when testing for genetic differentiation and when comparing frequencies distributions between different pairs of populations. Therefore, caution should be taken when using a common pooled database with general forensic purposes in Argentina. # 2006 Published by Elsevier Ireland Ltd. Keywords: STR; Population genetics; Population substructure; Argentina; Forensic; Databases

1. Introduction The continental territory of Argentina covers a vast surface of 2,766,890 km2. The country is divided into 23 provinces and one autonomous district (formerly Capital Federal). Its current population is about 36 million inhabitants (according to INDEC, http://www.indec.mecon.ar, 01/06/05), with more than 40% living in Buenos Aires province and in the autonomous district. Argentines are the result of a genetic and cultural fusion of diverse national and ethnic groups, with descendants of, mainly, Italian and Spanish immigrants. During the late 19th and early 20th centuries, Argentina received waves of immigrants from many European countries, who settled mainly in the city areas. The indigenous population, estimated nowadays at nearly 200,000 [1], is concentrated in several

* Corresponding author. Tel.: +54 11 4378 1205; fax: +54 11 4383 1197. E-mail address: [email protected] (E. Raimondi). 0379-0738/$ – see front matter # 2006 Published by Elsevier Ireland Ltd. doi:10.1016/j.forsciint.2006.02.042

communities in the provinces of the northwest, central and south regions of the country. Since the Native American and the immigrant contribution to the genetic pool may differ among cities from distinct regions, it is important to know whether significant genetic differences exist between urban populations from different regions of the country that could have implications in forensic casework and paternity testing. A large amount of data on autosomal genetic markers has become available from different regions of Argentina [2–12] since the beginning of its use in forensic genetics, mainly reported as frequency data from individual populations, with information about Hardy–Weinberg equilibrium (HWE) and other parameters of forensic interest. Published data on population comparisons mostly focused on anthropological issues that aimed to determine the Native American contribution to the genetic pool of the studied populations [2,3,12]. None of these studies have aimed to comprehensively analyze the level of population substructure in the Argentinian region and its forensic implications, and most of them only provided data on a limited

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number of STR markers. Here we present data for STR markers widely used in forensic genetics (D3S1358, HUMTH01, D21S11, D18S51, PENTA E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA D, HUMvWA, D8S1179, HUMTPOX, FGA), and a more extensive investigation of the genetic background of different Argentinian urban populations in order to determine if a common, pooled urban database, could be reliably used in Argentina with general forensic purposes. 2. Materials and methods 2.1. Sample collection and populations Blood samples (EDTA collected or absorbed onto filter paper cards) or bucal swabs were taken from 1734 unrelated males and females residing in urban areas of cities located in six distant provinces from northwest, central and south regions of Argentina, namely Buenos Aires (N = 879), Neuque´n (N = 355), Tucuma´n (N = 75), San Luis (N = 61), Santa Cruz (N = 132) and La Pampa (N = 232) (Fig. 1). Buenos Aires and Neuque´n are extended areas of populations of previously reported data [6,9]. Abbreviations used in tables and figures for the provinces names are: BA for Buenos Aires, NQ for Neuque´n, TU for Tucuma´n, SL for San Luis, SC for Santa Cruz, LP for La Pampa, and AR for pooled data from the six provinces.

2.2. DNA extraction and typing DNA was extracted from either 25 ml of whole blood or approximately 25 mm2 of bloodstained paper samples by means of Chelex1 100 resin [13]. Alternatively, DNA from bucal swabs was extracted by means of Proteinase K digestion followed by salting out procedure and ethanol precipitation. The 15 autosomal STRs (D3S1358, HUMTH01, D21S11, D18S51, PENTA E, D5S818, D13S317, D7S820, D16S539, CSF1PO, PENTA D, HUMvWA, D8S1179, HUMTPOX, FGA) were analyzed using the Powerplex1 16 System kit (Promega Corporation). Polymerase chain reaction (PCR) was performed according to manufacturer’s instruction, with 1 ml of extracted DNA (1–10 ng DNA), adjusting the reaction volume to 5 ml. Detection of amplified fragments was done using an ABI PRISM1 377 DNA Sequencer (ABApplied Biosystems), according to section VIII in Powerplex1 16 System technical manual [14]. Runs data were collected with ABI PRISM 377XL v2.0 (AB Applied Biosystems) and analyzed using Genescan Analysis software v3.1 (AB Applied Biosystems) and Genotyper Analysis software v1.1 (AB Applied Biosystems). Alleles were assigned according to the reference ladder provided with the Powerplex1 16 System kit. 2.3. Statistical analysis We have estimated allele frequencies, matching probability (MP) and a priori chance of exclusion (CE). Exact tests for HWE were computed for each individual population and for the pooled data. Genetic comparisons were conducted by means of exact test for population differentiation and by means of Fst tests (Slatkin’s distance) for every possible pair of populations. Locus by locus comparisons were also performed, including data in literature from other Argentinian provinces, Co´rdoba [8,11], Entre Rı´os [7], Santa Fe [10], one Native American population from La Puna, Argentina [15], and three European populations, Spain [16], Portugal [17–19] and Italy [20]. Neighbor-Joining trees (NJ) were built: (a) using the Fst distance matrix (Slatkin’s) obtained from genotypic data of the six studied populations and (b) using a frequency data matrix from the ten loci shared by the six populations, La Puna [15] and the three European populations [16–20]. Nei and Reynolds genetic distances were used. Tree robustness was assessed by carrying out 1000 bootstrap iterations. Exact test for HWE and population differentiation, Fst genetic distances (Slatkin’s distance), gene frequency estimates and single locus comparisons were performed using Arlequin Software [21]. A significance level of 0.05 was considered for all tests. When applicable, Bonferroni’s procedure was used to correct for multiple test. NJ and bootstrapping were performed using the Phylip package [22]. MP and CE were calculated with Powerstats v1.2 (Promega Corporation). 3. Results and discussion

Fig. 1. Geographical localization of the six studied provinces.

Allele frequency distributions estimated for all 15 loci in the six populations and for pooled data are provided as supplementary data (Tables 1–7 in Appendix A). Exact tests

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Table 1 P-values of the exact test for Hardy-Weinberg equilibrium (P-value), matching probability (MP) and a priori chance of exclusion (CE), at each loci in all studied population and in pooled data LOCUS

BA (N = 879)

NQ (N = 355)

TU (N = 75)

SL (N = 61)

SC (N = 132)

LP (N = 232)

AR (N = 1734)

D3S1358 P-value MP CE

0.4034 0.0888 0.5818

0.9045 0.1021 0.5681

0.3520 0.1353 0.3417

0.1396 0.1072 0.4620

0.5862 0.0986 0.4717

0.9327 0.0935 0.5550

0.8441 0.0929 0.5506

TH01 P-value MP CE

0.9508 0.0758 0.5735

0.8016 0.0843 0.5182

0.6599 0.0983 0.4817

0.8386 0.0938 0.5455

0.7209 0.1001 0.5098

0.1303 0.0887 0.5397

0.7385 0.0796 0.5475

D21S11 P-value MP CE

0.4732 0.0423 0.6722

0.4601 0.0539 0.7130

0.7236 0.0560 0.8091

0.5324 0.0524 0.6676

0.8281 0.0492 0.6919

0.6094 0.0469 0.7447

0.7025 0.0438 0.6973

D18S51 P-value MP CE

0.4292 0.0295 0.7193

0.3116 0.0313 0.7639

0.2229 0.0475 0.6494

0.9071 0.0449 0.7323

0.6605 0.0379 0.6047

0.0917 0.0342 0.7796

0.1786 0.0296 0.7247

PENTA E P-value MP CE

0.4126 0.0181 0.8278

0.4537 0.0167 0.8619

0.8571 0.0279 0.8364

0.3143 0.0427 0.6676

0.2624 0.0228 0.8140

0.6188 0.0207 0.7708

0.0991 0.0167 0.8207

D5S818 P-value MP CE

0.7282 0.1219 0.4693

0.1357 0.1197 0.4755

0.6716 0.1097 0.5270

0.3598 0.1443 0.3408

0.0545 0.1350 0.5630

0.6260 0.1422 0.4060

0.5058 0.1214 0.4660

D13S317 P-value MP CE

0.6403 0.0617 0.6092

0.2923 0.0525 0.6254

0.7202 0.0649 0.6753

0.9957 0.0497 0.6997

0.4198 0.0537 0.6623

0.9821 0.0599 0.7017

0.2273 0.0553 0.6346

D7S820 P-value MP CE

0.2183 0.0711 0.5612

0.8508 0.0869 0.5529

0.0382 0.0930 0.4817

0.7901 0.1153 0.7323

0.1339 0.0868 0.5360

0.5215 0.0765 0.5321

0.0265 0.0760 0.5557

D16S539 P-value MP CE

0.3676 0.0827 0.6007

0.4266 0.0858 0.6148

0.7188 0.0812 0.6494

0.5732 0.0868 0.6051

0.1631 0.0830 0.4594

0.6012 0.0893 0.5784

0.4690 0.0813 0.5914

CSF1PO P-value MP CE

0.6108 0.1277 0.4583

0.7372 0.1292 0.4526

0.2139 0.1228 0.4180

0.1062 0.1696 0.4890

0.3373 0.1251 0.3895

0.4058 0.1210 0.3745

0.7900 0.1246 0.4392

PENTA D P-value MP CE

0.4569 0.0444 0.6590

0.2440 0.0495 0.6095

0.2451 0.0823 0.7014

0.7306 0.0556 0.6360

0.9430 0.0581 0.6770

0.3000 0.0499 0.6429

0.5435 0.0460 0.6489

vWA P-value MP CE

0.9452 0.0739 0.5449

0.6387 0.0824 0.5429

0.9425 0.0748 0.5745

0.4859 0.0804 0.4359

0.7381 0.0871 0.5767

0.6233 0.0713 0.6347

0.7075 0.0736 0.5557

D8S1179 P-value MP CE

0.4724 0.0573 0.5777

0.3577 0.0702 0.6095

0.7527 0.0663 0.5745

0.8543 0.0943 0.6676

0.2364 0.0775 0.6477

0.8995 0.0664 0.6763

0.5660 0.0615 0.6053

TPOX P-value

0.5633

0.0710

0.9639

0.8020

0.1379

0.9000

0.6277

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Table 1 (Continued ) LOCUS

BA (N = 879)

NQ (N = 355)

TU (N = 75)

SL (N = 61)

SC (N = 132)

LP (N = 232)

AR (N = 1734)

0.1689 0.3625

0.1575 0.3840

0.1829 0.2909

0.1604 0.3865

0.2042 0.2801

0.1439 0.4527

0.1622 0.3688

FGA P-value MP CE

0.1163 0.0291 0.7353

0.9421 0.0271 0.7582

0.6174 0.0379 0.6753

0.2042 0.0460 0.8324

0.4399 0.0301 0.7677

0.2998 0.0334 0.8237

0.0814 0.0267 0.7549

Global MP

7.965  10

Global CE

0.999999

MP CE

19

1.793  10

18

0.999999

1.844  10

17

3.827  10

0.999999

17

0.999999

6.614  10

18

2.201  10

0.999999

18

7.462  10

0.999999

19

0.999999

Abbreviations for provinces names are in Section 2.

Table 2 Fst (Slatkin’s distance) (below diagonal) and Fst P-values (above diagonal) between populations from the six different provinces of Argentina studied in this work, using 15 loci genotypes information

showed no deviation of HWE (Table 1), neither for individual populations nor for the global sample. Individual and global MP and CE calculated for each studied population are indicated in Table 1. Global values for MP ranged from 1.844  10 17 (TU) to 7.965  10 19 (BA), while global CE was always 0.999999. In spite of these results, databases from San Luis and Tucuma´n are not intended for forensic use because of the low number of tested individuals in these populations. Therefore, those data are reported for comparisons purposes only. Regarding population differentiation, exact tests showed no significant P-values among the different pairs of populations (0.130 < P < 1.000). Interestingly, Fst computed by means of Slatkin’s distance, yielded statistically significant differences when comparing Buenos Aires with Neuque´n, Tucuma´n, San Luis and Santa Cruz; Neuque´n with Buenos Aires, Tucuma´n and La Pampa; Tucuma´n with Buenos Aires and La Pampa (Table 2), although the corresponding Fst values were low. NJ tree of Fig. 2a was built with the distance matrix in Table 2 for the six studied populations. The tree shows Neuque´n, San Luis, Santa Cruz and Tucuma´n grouped in one cluster, and Buenos Aires and La Pampa in the opposite side. When comparing Argentinian urban populations at the single locus level, significant P-values were observed between the following pairs of populations: (1) Buenos Aires and

BA BA NQ TU SL SC LP *

NQ

– 0.003 0.003 0.002 0.003 0.000

TU

0.000 – 0.002 0.001 0.000 0.001

*

SL *

0.000 0.024* – 0.001 0.000 0.002

0.004 0.230 0.241 – 0.000 0.000

SC *

0.000 0.431 0.692 0.393 – 0.001

LP *

0.629 0.002* 0.011* 0.377 0.050 –

Significant P-values. Abbreviations for provinces names are in Section 2.

Neuquen at D5S818 (P = 0.000), FGA (P = 0.001) and PENTA E (P = 0.002) loci; (2) Buenos Aires and Santa Cruz at D5S818 (P = 0.025) and D13S317 (P = 0.016) loci; (3) Buenos Aires and Tucuma´n at D13S317 locus (P = 0.029); (4) Neuquen and La Pampa at D5S818 locus (P = 0.011); (5) Neuquen and Co´rdoba [8] at FGA locus (P = 0.041); (6) Tucuma´n and Co´rdoba [11] at D3S1358 (P = 0.015); (7) Tucuma´n and Rosario [10] at TPOX locus (P = 0.016); (8) Santa Cruz and La Pampa at D5S818 locus (P = 0.041); (9) Santa Cruz and Rosario at TPOX locus (P = 0.027). Pooled data yielded significant differences with Neuquen at D5S818 (Table 3).

Table 3 P-values for single locus comparisons between pooled data from Buenos Aires, Neuque´n, Tucuma´n, San Luis, Santa Cruz and La Pampa, and the individual studied populations, other provinces from Argentina, and Spain, Italy and Portugal LOCUS D3S1358 THO1 D21S11 D18S51 PENTA E D5S818 D13S317 D7S820 D16S539 CSF1PO PENTA D VWA D8S1179 TPOX FGA

BA 0.946 0.627 0.998 1.000 0.950 0.554 0.434 0.934 0.992 0.985 0.977 0.904 0.975 0.796 0.867

NQ 0.867 0.820 0.675 0.972 0.240 0.026* 0.852 0.855 0.842 0.408 0.565 0.551 0.967 0.487 0.101

TU 0.297 0.242 0.819 0.937 0.993 0.568 0.087 0.828 0.531 0.386 0.561 0.790 0.707 0.114 0.955

SL 0.410 0.953 0.500 0.937 0.890 0.910 0.657 0.666 0.999 0.220 0.697 0.351 0.561 0.415 0.894

SC 0.267 0.536 0.897 0.945 0.539 0.239 0.269 0.969 0.960 0.722 0.950 0.925 0.499 0.479 0.389

LP 0.933 0.721 0.936 0.946 0.994 0.627 0.767 0.988 0.440 0.989 0.784 0.278 0.821 0.794 0.926

RO 0.332 – – – – 0.928 0.828 0.181 0.159 – 0.747 – 0.248 –

CD1 0.950 – 0.825 0.954 – 0.970 0.953 0.495 – – – 0.669 0.755 – 0.304

CD2 0.662 – 0.474 0.835 – 0.730 0.963 0.666 – – – 0.285 0.827 – 0.425

ER 0.396 – – – – 0.370 0.899 0.567 0.553 – 0.951 – 0.782 –

PU *

0.000 0.000* 0.004* 0.000* – 0.000* 0.000* 0.001* – – – 0.002* 0.311 – 0.000*

IT

ES

PO

0.223 0.048* 0.905 0.872 0.422 0.007* 0.030* 0.072 0.337 0.802 0.242 0.089 0.852 0.059 0.090

0.063 0.001* 0.185 0.194 0.342 0.000* 0.001* 0.000* 0.047* 0.701 0.167 0.525 0.170 0.142 0.044*

0.274 0.000* 0.022* 0.197 0.794 0.000* 0.000* 0.007* 0.076 0.858 0.116 0.116 0.656 0.001* 0.000*

* Significant P-values; RO: Rosario [10]; CD1: Co´rdoba [8]; CD2: Co´rdoba [11]; ER: Entre Rı´os [7]; PU: La Puna [15]; IT: Italy [20]; ES: Spain [16]; PO: Portugal [17–19]; other abbreviations are in Section 2.

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loci between published data from La Puna and those from different urban populations was different; therefore, the number of significant differences observed with each individual population varied. Thus, for example, one group of populations (Buenos Aires, Neuque´n, La Pampa and pooled data from the six studied provinces) showed significant differences with La Puna at 9 out of 10 compared loci (90%), Rosario [10] and Entre Rı´os [7] were statistically different at the four loci shared with La Puna (100%), while Tucuma´n exhibited the lowest number of differences with La Puna, with six out of ten compared loci (60%). Consistently, NJ tree of Fig. 2b shows La Puna [15] more closely related to Tucuma´n than to the other urban populations. Globally, differences between La Puna and the urban populations were observed at more than 81% of the compared loci. Regarding European populations, some significant Pvalues were obtained at locus by locus comparisons with different Argentinian urban populations and with pooled data from Buenos Aires, Neuque´n, Tucuma´n, La Pampa, San Luis and Santa Cruz (Table 9 in Apendix A), being Neuque´n the one with the maximum number of significant differences with the three European populations. NJ tree (Fig. 2b) shows that Buenos Aires and La Pampa are closer to the extreme of the tree where the three European populations are grouped together. This fact likely reflects their higher proportion of European ancestry in comparison to other studied regions in Argentina. No substantial differences in the shape and splitting pattern of the tree were observed when applying both Nei and Reynolds distances (data not shown). It is noteworthy that bootstrap values were high at every branch of the tree. 4. Final remarks

Fig. 2. (a) Neighbor-Joining tree based on Slatkin’s distances for the six studied populations. Abbreviations for provinces names are in Section 2. (b) NeighborJoining tree based on gene frequencies from the six Argentinian urban populations, La Puna and three European populations. IT: Italy; ES: Spain; PO: Portugal; PU: La Puna. Other abbreviations for population names are in Section 2.

Not surprisingly, locus by locus comparison tests between La Puna [15] and data from urban populations, including published data, yielded statistically different P-values at all loci except D8S1179 (Table 8 in Apendix A). The number of shared

These analyses show that, given the differences observed in frequencies distributions at several loci, and the significant Fst values between different urban populations, a general forensic database for autosomal loci in Argentina might not be appropriate, provided the reference population is urban. As differences observed at frequencies distributions are substantially greater between urban populations and the Native American community of La Puna [15] than among urban populations, special attention should be paid concerning small isolates where the Native component is much more important. It is also remarkable that, when comparing Argentinian urban populations with two Iberian samples [16–19] and one Italian sample [20] (both, the major European population genetic contributors to Argentina), substantial significant differences were detected, so that, an Iberian or Italian database may not adequately represent the Argentinian genetic makeup. We are aware that some caution is needed with respect to these final conclusions, given the small sample size of some of the samples used in the present report. These provisional findings support, however, the use of local databases, whenever possible, for paternity testing and forensic casework in Argentina.

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Acknowledgements This work was partially supported by Fundac¸a˜o para a Cieˆncia e a Tecnologia (POCTI, Programa Operacional Cieˆncia, Tecnologia e Inovac¸a˜o). Support of the ‘Ramo´n y Cajal’ Spanish programme from the Ministerio de Educacio´n y Ciencia to AS is gratefully acknowledged (RYC2005-3), as well as grants from the Ministerio de Sanidad y Consumo (PI030893; SCO/3425/2002) and Genoma Espan˜a (CeGen; Centro Nacional de Genotipado). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.forsciint. 2006.02.042. References [1] C. Martı´nez-Sarasola, Nuestros paisanos los indios, Emece´, Buenos Aires, 2005. [2] A. Sala, G. Penacino, D. Corach, VNTR polymorphism in the Buenos Aires, Argentina, metropolitan population, Hum. Biol. 69 (1997) 777– 783. [3] A. Sala, G. Penacino, D. Corach, Comparison of allele frequencies of eight STR loci from Argentinian Amerindian and European populations, Hum. Biol. 70 (1998) 937–947. [4] A. Sala, G. Penacino, R. Carnese, D. Corach, Reference database of hypervariable genetic markers of Argentina: application for molecular anthropology and forensic casework, Electrophoresis 20 (1999) 1733– 1739. [5] N. Tourret, J. Lo´pez-Camelo, L. Vidal-Rioja, Allele frequencies of six STR loci in Argentine populations, Forensic Sci. Int. 44 (1999) 1265–1269. [6] G. Berardi, U. Toscanini, E. Raimondi, STR data for PowerPlex 16 System from Buenos Aires population, Argentina, Forensic Sci. Int. 134 (2003) 222–224. [7] G. Martı´nez, E. Va´zquez, C. Schaller, N. Quevedo, Genetic data on 11 STRs (CSF1PO, TPOX, TH01, F13A01, FESFPS, vWA, D16S539, D7S820, D13S317, F13B, LPL) in an Argentine northeast population, Forensic Sci. Int. 133 (2003) 254–255.

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