Allele frequencies in Azuay Population in Ecuador
Received: August 11, 2017
Accepted: September 15, 2017
Published: September 27, 2017
Genet.Mol.Res. 16(3): gmr16039797
DOI: 10.4238/gmr16039797
Abstract
One hundred and eighty-two samples of unrelated people who requested the paternity test at the Molecular Biology and Genetics Laboratory of the Catholic University of Cuenca-Ecuador in the province of Azuay were studied, except for the D1S1656 (180 samples) and SE33 (89 samples) markers. The STRs D22S1045, D3S1358, VWA, D16S539, D2S1338, D8S1179, D21S11, D18S51, D19S433, TH01, FGA, D1S1656, D12S391, D10S1248, D2S441, and SE33 were typed from blood samples, amplifying the DNA by polymerase chain reactions and electrophoresis. The allele frequencies were estimated by simple counting and the impartial heterozygosity was also calculated. The Hardy-Weinberg equilibrium theory was studied. In the results obtained with the analyzed markers, the largest number of alleles can be observed in the markers with the highest polymorphic information content (PIC): D21S11, D16S539, D2S1338, D19S433, D18S51, FGA, D1S1656, and D12S391. In addition, SE33 was analyzed in certain samples, showing as result a high PIC, in fact, the highest one because of its great polymorphisc characteristic. Likewise, these markers are the ones providing the highest probability of discrimination and the lowest probability of coincidence.
Introduction
Short tandem repeat (STR) markers were first described as effective tools for human identity testing in the early 1990’s (Butler, 2006). Over the past decade, the human identity testing community has settled on a set of core STR loci that are widely used for DNA typing applications (Butler, 2006). The usefulness of genetic markers for identity testing and paternity analysis is based on known allele frequencies for the genetic markers analyzed (Cifuentes et al., 2008). STR loci are short, repetitive sequences (3-7 base pairs in length) distributed throughout the human genome (Butler, 2005). A variety of commercial kits enable robust amplification of these core STR loci (Butler, 2006). NGM is a kit of PCR amplification of fifteen STR: D22S1045, D3S1358, VWA, D16S539, D2S1338, D8S1179, D21S11, D18S51, D19S433, TH01, FGA, D1S1656, D12S391, D10S1248, and D2S441, and the gender determination locus, Amelogenin (Applied Biosystems, 2015). The NGM SElect kit incorporates an additional STR, the SE33, to the mentioned above (Applied Biosystems, 2015).
The Province of Azuay is one of the 24 provinces forming up the Republic of Ecuador. The administrative capital city of Azuay is Cuenca, which is also the largest and most populated city of this province. Ecuadorian individuals originated from a racial miscegenation among ancestral indigenous groups with Spanish Caucasoid settlers and African descendants (Gaviria et al., 2013).
In addition to technical validation, the implementation of STRs requires population studies that include estimating various statistical parameters for forensic studies. At the intrapopulation level, the frequencies should be estimated as allelic frequencies and should verify the fact that the population in which the genetic analysis system will be used is in Hardy- Weinberg equilibrium, since this allows the use of the binomial squared formula to estimate the frequency of genotypes from allele frequencies. The associations between pairs of loci should be ruled out the linkage disequilibrium (DL), which allows the use of product rule in order to estimate the frequency of genetic profiles. In addition, it is convenient to estimate statistical parameters of forensic interest that indicates the expected utility a priori for each locus and for the genetic system such as: heterozygosity (HE), power of exclusion (PE), power of discrimination (PD), polymorphic information content (PIC), and index of paternity (IPT). On the other hand, at the inter-population level the validation of STRs usually includes the comparison with other populations to establish their genetic relationships, structure and even the knowledge of their origins (Martínez et al., 2016).
In summary, the forensic parameters and the population validation enable these systems to be used to estimate the frequency of a genetic profile, or to calculate the paternity probability in a criminal law case; when the alleged father and son agree on a paternity test. For this purpose, various population studies have been carried out and many databases have been generated throughout the world using STRs (Martínez et al., 2016).
Yet, there is no information published about gene frequencies of multiallelic loci in the population of Azuay, Ecuador. The present study describes the allele frequencies of fifteen and sixteen STR loci in this population.
Materials and Methods
The blood used in the paternity test was obtained from unrelated individuals residing in Azuay, Ecuador, through venipuncture and its collection in FTA classic cards. Later, these cards were perforated with a 1.2-mm micropuncher to obtain the DNA for the next step, the PCR amplification.
The PCR amplification of the first 15 genetic markers was made by using the NGM kit. Then, the NGM Select Kit was employed (including the SE33) for the amplification of 16 STRs.
The amplified samples were placed in the ABI3500 genetic analyzer and the Data Collection software helped to obtain the capylar electrophoresis results that were analyzed by the GenneMapper-IDX software.
Statistical analysis
The allele frequencies were determined and adjusted to the genotypic frequencies with EHW for each STR. The statistical parameters of forensic interest were determined using the PowerStats and GDA Softwares.
Results and Discussion
The allele frequencies of the 16 autosomal STRs were estimated and included in the SElect NGM. Although they are the most basic parameters, the allele frequencies are the most useful data employed by forensic geneticists for biostatistic interpretations of each paternity test and for forensic cases when there is an agreement. Among them, a minimum of allele frequency is essential to interpret cases of null or rare alleles that can be used as a benefit for the accused (Martínez et al., 2016).
In the processes of identifying or analyzing biological ties of kinship for forensic purposes, it is necessary to have the largest number of markers with the highest probability of discrimination to avoid random collation. In this case study, there are 9 markers that are highly polymorphic and among them, there is the SE33 that shows the highest degree of polymorphic information, although this is a high molecular weight marker that is present in many commercial kits. Therefore, in cases where a small amount of amplifiable DNA is obtained, either because of the intrinsic condition of the samples’ type (number of nucleated cells) or quality, which is mainly involved in the degree of degradation of the same DNA and/ or the presence of inhibitors, it will partially amplify or not, being the partial amplification the greatest risk, making it difficult to distinguish the homozygous state.
In the results obtained with the analyzed markers, the largest number of alleles can be observed in the markers with the highest PIC: D21S11, D16S539, D2S1338, D19S433, D18S51, FGA, D1S1656, and D12S391. In addition, SE33 was analyzed in certain samples, showing as a result a high PIC. In fact, the highest one because of its great polymorphism capacity. Likewise, these markers are those providing the highest probability of discrimination and the lowest probability of coincidence (Table 1).
ALL population | HE | HO | f |
---|---|---|---|
Locus | |||
D8S1179 | 0.770675 | 0.711111 | 0.077487 |
D21S11 | 0.839106 | 0.861111 | -0.026300 |
D3S1358 | 0.675905 | 0.677778 | -0.002778 |
TH01 | 0.676168 | 0.666667 | 0.014091 |
D16S539 | 0.788889 | 0.688889 | 0.127070 |
D2S1338 | 0.838440 | 0.850000 | -0.013826 |
D19S433 | 0.831492 | 0.805556 | 0.031277 |
vWA | 0.702368 | 0.733333 | -0.044216 |
D18S51 | 0.855308 | 0.855556 | -0.000290 |
FGA | 0.854968 | 0.833333 | 0.025373 |
D2S441 | 0.617936 | 0.594444 | 0.038118 |
D22S1045 | 0.579155 | 0.516667 | 0.108165 |
D10S1248 | 0.696735 | 0.683333 | 0.019287 |
D1S1656 | 0.873863 | 0.861111 | 0.014632 |
D12S391 | 0.816868 | 0.805556 | 0.013887 |
All | 0.761192 | 0.742963 | 0.024013 |
SE33* | 0.937980 | 0.876404 | 0.065995 |
Table 1. Structure of the population at the level of heterozygosity of 15 markers not including SE33.
Hardy-Weimberg equilibrium and linkage imbalance
When analyzing the Hardy-Weimberg equilibrium, it is observed that at a level of 0.05 there is no equilibrium in the markers D8S1179, D16S539, and SE 33, but when the limit is 0.01, the only one that has a highly significant imbalance is the D16S539 marker (Table 2).
Population # 1 (Azuay) of 180 individuals | Prob | Locus combination |
---|---|---|
Runs | ||
3200* | 0.035000* | D8S1179* |
3200 | 0.481875 | D21S11 |
3200 | 0.946250 | D3S1358 |
3200 | 0.730625 | TH01 |
3200* | 0.001250* | D16S539* |
3200 | 0.757500 | D2S1338 |
3200 | 0.286250 | D19S433 |
3200 | 0.350000 | vWA |
3200 | 0.941875 | D18S51 |
3200 | 0.402500 | FGA |
3200 | 0.379375 | D2S441 |
3200 | 0.058125 | D22S1045 |
3200 | 0.633125 | D10S1248 |
3200 | 0.545000 | D1S1656 |
3200 | 0.618750 | D12S391 |
Population # 1 (Azuay) of 89 individuals | ||
Runs | Prob | Locus combination |
3200* | 0.033750* | SE33* |
Table 2. Balance Hardy Weimberg.
In the case of linkage disequilibrium, there is a very significant imbalance in the D16S539 marker that can be observed, and could be expected in populations, where historically, there have been large foreign components or in cases of miscegenation (Loh et al., 2013).
Deficit and excess heterozygotes in the study population
When observing the analized makers it can be seen that a total of 5 markers have
excess of heterozygotes while the rest have heterozygotes deficit, being D16S539 the marker with the greatest value of heterozygotes deficit. (Table 1). Likewise, heterozygosity as a value is the highest in the most polymorphic markers, except for the D16S539 marker (Frequency Table 3). The general tendency of a slight heterozygote deficit is observed.
Alleles | D8S1179 1 | D21S11 1 | D3S1358 1 | TH01 1 | D16S539 1 | D2S1338 1 | D19S433 1 | vWA 1 | D18S51 1 | FGA 1 | D2S441 | D22S1045 | D10S1248 | D1S1656 | D12S391 | SE33 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 0.3379 | |||||||||||||||
7 | 0.4313 | |||||||||||||||
8 | 0.0027 | 0.0357 | 0.0055 | |||||||||||||
9 | 0.0027 | 0.0495 | 0.2225 | 0.0027 | ||||||||||||
9.2 | 0.0027 | 0.0027 | ||||||||||||||
9.3 | 0.1429 | |||||||||||||||
10 | 0.1154 | 0.0027 | 0.2637 | 0.0165 | 0.5632 | 0.0056 | ||||||||||
11 | 0.0604 | 0.1676 | 0.0165 | 0.022 | 0.2088 | 0.0275 | 0.0139 | 0.0056 | ||||||||
11.3 | 0.0275 | |||||||||||||||
12 | 0.1538 | 0.0027 | 0.2418 | 0.0302 | 0.0027 | 0.0687 | 0.0165 | 0.0027 | 0.0137 | 0.0639 | 0.0112 | |||||
12.2 | 0.011 | |||||||||||||||
13 | 0.3791 | 0.0027 | 0.0797 | 0.217 | 0.0082 | 0.1044 | 0.011 | 0.0027 | 0.2335 | 0.1306 | ||||||
13.2 | 0.1126 | |||||||||||||||
13.3 | 0.0027 | |||||||||||||||
14 | 0.206 | 0.0412 | 0.0165 | 0.2857 | 0.0192 | 0.272 | 0.1429 | 0.0055 | 0.4203 | 0.1139 | 0.0337 | |||||
14.2 | 0.0467 | |||||||||||||||
15 | 0.0604 | 0.4753 | 0.0027 | 0.1401 | 0.0797 | 0.1538 | 0.0275 | 0.4066 | 0.2637 | 0.1361 | 0.0055 | 0.0506 | ||||
15.2 | 0.0742 | |||||||||||||||
15.3 | 0.0111 | |||||||||||||||
16 | 0.0165 | 0.272 | 0.0082 | 0.0302 | 0.3846 | 0.1099 | 0.5 | 0.0632 | 0.2028 | 0.0357 | 0.0674 | |||||
16.2 | 0.0302 | |||||||||||||||
16.3 | 0.0528 | |||||||||||||||
17 | 0.1538 | 0.1621 | 0.3626 | 0.1236 | 0.011 | 0.0495 | 0.0055 | 0.0583 | 0.0247 | 0.1236 | ||||||
17.2 | 0.0027 | |||||||||||||||
17.3 | 0.1639 | 0.0082 | ||||||||||||||
18 | 0.0467 | 0.0824 | 0.1016 | 0.0659 | 0.0137 | 0.0055 | 0.0056 | 0.1951 | 0.0506 | |||||||
18.3 | 0.0361 | 0.0027 | ||||||||||||||
19 | 0.0055 | 0.228 | 0.0357 | 0.0192 | 0.0495 | 0.1868 | 0.1011 | |||||||||
19.2 | 0.0112 | |||||||||||||||
19.3 | 0.0111 | 0.0275 | ||||||||||||||
20 | 0.1951 | 0.0055 | 0.011 | 0.033 | 0.3104 | 0.0449 | ||||||||||
21 | 0.0247 | 0.0165 | 0.0742 | 0.1016 | 0.0169 | |||||||||||
21.2 | 0.0056 | |||||||||||||||
22 | 0.044 | 0.0082 | 0.0824 | 0.0412 | ||||||||||||
22.2 | 0.0112 | |||||||||||||||
23 | 0.1868 | 0.0055 | 0.1291 | 0.0357 | ||||||||||||
23.2 | 0.0056 | |||||||||||||||
24 | 0.0467 | 0.0027 | 0.1978 | 0.0137 | ||||||||||||
24.2 | 0.0169 | |||||||||||||||
25 | 0.0165 | 0.228 | 0.011 | |||||||||||||
25.2 | 0.0337 | |||||||||||||||
26 | 0.0055 | 0.1538 | ||||||||||||||
26.2 | 0.0169 | |||||||||||||||
27 | 0.0027 | 0.0165 | ||||||||||||||
27.2 | 0.073 | |||||||||||||||
28 | 0.0742 | 0.0082 | ||||||||||||||
28.2 | 0.0955 | |||||||||||||||
29 | 0.1511 | |||||||||||||||
29.2 | 0.0674 | |||||||||||||||
30 | 0.261 | |||||||||||||||
30.2 | 0.0385 | 0.0787 | ||||||||||||||
31 | 0.0659 | |||||||||||||||
31.2 | 0.1978 | 0.0506 | ||||||||||||||
32 | 0.011 | |||||||||||||||
32.2 | 0.1346 | 0.0112 | ||||||||||||||
33.2 | 0.0604 | 0.0056 | ||||||||||||||
34.2 | 0.0056 | |||||||||||||||
35.2 | 0.0027 | |||||||||||||||
Homozygotes | 0.29 | 0.14 | 0.32 | 0.34 | 0.31 | 0.15 | 0.2 | 0.26 | 0.14 | 0.16 | 0.41 | 0.48 | 0.32 | 0.14 | 0.19 | 0.12 |
Heterozygotes | 0.71 | 0.86 | 0.68 | 0.66 | 0.69 | 0.85 | 0.8 | 0.74 | 0.86 | 0.84 | 0.59 | 0.52 | 0.68 | 0.86 | 0.81 | 0.88 |
Total Alleles | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 364 | 360 | 364 | 178 |
Probability ofcoincidence | 0.0852 | 0.0509 | 0.1618 | 0.1539 | 0.0749 | 0.0509 | 0.0533 | 0.1524 | 0.0369 | 0.0404 | 0.1623 | 0.2501 | 0.1472 | 0.0348 | 0.0604 | 0.0213 |
Power ofdiscrimination | 0.9148 | 0.9491 | 0.8382 | 0.8461 | 0.9251 | 0.9491 | 0.9467 | 0.8476 | 0.9631 | 0.9596 | 0.8377 | 0.7499 | 0.8528 | 0.9652 | 0.9396 | 0.9787 |
Polymorphic Information Content | 0.7392 | 0.8171 | 0.6224 | 0.6185 | 0.7542 | 0.8164 | 0.808 | 0.6527 | 0.8385 | 0.8353 | 0.5731 | 0.496 | 0.6411 | 0.8582 | 0.7919 | 0.9271 |
Probability of exclusion | 0.4507 | 0.7199 | 0.3919 | 0.376 | 0.4165 | 0.6981 | 0.6031 | 0.4866 | 0.709 | 0.6658 | 0.2766 | 0.2075 | 0.4 | 0.7169 | 0.6134 | 0.7475 |
Typical paternity index | 1.75 | 3.64 | 1.5424 | 1.4918 | 1.625 | 3.3704 | 2.5278 | 1.8958 | 3.5 | 3.0333 | 1.2133 | 1.046 | 1.569 | 3.6 | 2.6 | 4.0455 |
Minimum allele frequency | 0.0151 | 0.017 | 0.0147 | 0.0146 | 0.0149 | 0.0168 | 0.0161 | 0.0153 | 0.0169 | 0.0165 | 0.0139 | 0.0134 | 0.0148 | 0.0171 | 0.0162 | 0.0339 |
Hardy-Weinberg Equilibrium | 0.035 | 0.4819 | 0.9463 | 0.7306 | 0.0013 | 0.7575 | 0.2863 | 0.35 | 0.9419 | 0.4025 | 0.3794 | 0.0581 | 0.6331 | 0.545 | 0.6188 | 0.0338 |
Table 3. Allelic frequencies, forensic parameters and of genetic structure in the population of Azuay.
Acknowledgments
The authors present our acknowledgments to the Directors of the Universidad Católica de Cuenca (Cuenca-Ecuador) for the help and the economic support provided for the culmination of this investigative work, also to all the individuals who authorized the use of their samples for this study.
About the Authors
Corresponding Author
P.P. Orellana
Academic Unit of Health and Welfare, Odontology Career, Laboratory of Molecular Biology and Genetics, Catholic University of Cuenca, Cuenca, Ecuador
- Email:
- porellana@ucacue.edu.ec
References
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