
==== Front
BMC Genomics
BMC Genomics
BMC Genomics
1471-2164
BioMed Central London

10762
10.1186/s12864-024-10762-9
Research
Characterization of sequence variations in the extended flanking regions using massively parallel sequencing in 21 A-STRs and 21 Y-STRs
Li Hui 12
Li Beixu 4
Liu Yanan 56
Yang Fan 5
Cao Yu 5
Xie Jianhui 2
Liu Xiling 1
Zhao Zhenmin zhaozm@ssfjd.cn

1
Li Chengtao lichengtao@fudan.edu.cn

3
1 https://ror.org/00anm2x55 grid.419906.3 0000 0004 0386 3127 Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063 China
2 https://ror.org/013q1eq08 grid.8547.e 0000 0001 0125 2443 Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai, 200032 China
3 grid.8547.e 0000 0001 0125 2443 Shanghai Medical College, Fudan University, Shanghai, 200032 China
4 https://ror.org/032gae017 grid.449641.a 0000 0004 0457 8686 School of Policing Studies, Shanghai University of Political Science and Law, Shanghai, 201701 China
5 https://ror.org/04ry60e05 grid.464363.0 Key Laboratory of Forensic Evidence and Science Technology, Ministry of Public Security, Institute of Forensic Science, Shanghai, 200083 China
6 https://ror.org/013q1eq08 grid.8547.e 0000 0001 0125 2443 Ministry of Education’s Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433 China
7 9 2024
7 9 2024
2024
25 8419 1 2024
2 9 2024
© The Author(s) 2024
2024
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In forensic genetics, utilizing massively parallel sequencing (MPS) to analyze short tandem repeats (STRs) has demonstrated several advantages compared to conventional capillary electrophoresis (CE). Due to the current technical limitations, although flanking region polymorphisms had been mentioned in several previous studies, most studies focused on the core repeat regions of STRs or the variations in the adjacent flanking regions. In this study, we developed an MPS system consisting of two sets of multiplex PCR systems to detect not only the STR core repeat regions but also to observe variants located at relatively distant positions in the flanking regions. The system contained 42 commonly used forensic STRs, including 21 autosomal STRs (A-STRs) and 21 Y-chromosomal STRs (Y-STRs), and a total of 350 male individuals from a Chinese Han population were genotyped. The length and sequence variants per locus were tallied and categorized based on length (length-based, LB), sequence without flanking region (core repeat regions sequence-based, RSB), and sequence with flanking region (core repeat and flanking regions sequence-based, FSB), respectively. Allele frequencies, Y-haplotype frequencies, and forensic parameters were calculated based on LB, RSB, and FSB, respectively, to evaluate the improvement in discrimination power, heterozygosity, and effectiveness of forensic systems. The results suggested the sequence variations have more influence on A-STRs and could improve the identification ability of MPS-STR genotyping. Concordance between MPS and CE methods was confirmed by using commercial CE-based STR kits. The impact of flanking region variations on STR genotype analysis and potential factors contributing to discordances were discussed. A total of 58 variations in the flanking regions (53 SNPs/SNVs and 5 InDels) were observed and most variations (48/58) were distributed in A-STRs. In summary, this study delved deeper into the genetic information of forensic commonly used STR and advanced the application of massively parallel sequencing in forensic genetics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-024-10762-9.

Keywords

Massively parallel sequencing
Flanking region
Sequence variations
Concordance
Compound marker
Public Interest Research Grant Programs of National Research InstitutesGY2024G-3 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81930056 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

Massively parallel sequencing (MPS), also referred to as next-generation sequencing (NGS), has recently gained significant attention for its immense potential in the field of forensic genetics [1–3]. Short tandem repeat (STR), a forensic genetic marker, has gained widespread recognition in criminal investigations and court trials over the past two decades [4, 5]. The utilization of MPS for STR analysis has demonstrated numerous advantages in several previous studies [6, 7]. Compared to conventional capillary electrophoresis (CE), MPS can analyze more STR loci simultaneously without limitation by restrictions in size-based separation or fluorescence dye detection [8, 9]. Besides, the sequence variations within the core repeat and flanking regions of STRs could be identified by sequencing and significantly improved the identification ability of STRs [10–14]. Currently, the mainstream MPS platforms used in forensic were usually the long-reads sequencing platforms, including Novaseq™ (PE250, Illumina, San Diego, CA, USA), MiSeq™ System (PE300, Illumina), MiSeq FGx™ System (PE300, Verogen, San Diego, CA, USA), Ion torrent PGM (SE400, Thermo Fisher, Foster City, CA, USA), Ion torrent S5 (SE400, Thermo Fisher), and MGISEQ-2000 platform (SE400, MGI Tech, Shenzhen, Guangdong, China) [15]. Due to the current technical limitations, the quality of the MPS sequencing read at its end tends to be comparatively lower so that there are restrictions on the length of reads in the flanking regions of STRs. Despite the mention of flanking region polymorphisms in several previous studies [16–26], the majority of research has primarily focused on the core repeat regions of STRs or variations in the adjacent flanking regions. Consequently, the current state of research lacks systematic investigations into the variations in flanking regions, particularly those occurring at more distal positions from the core repeat regions. The sequence variants could also improve the STR performance in forensic application [13] and were likely associated with bioinformatics analysis [14], even caused discordances between MPS and CE methods in some cases [12]. Thus, the flanking region variations in commonly used forensic STRs need to be further explored.

For current commercial MPS STR panels, such as ForenSeq™ DNA Signature Prep Kit (Verogen) and Precision ID GlobalFiler™ NGS STR Panel v2 (Thermo Fisher), the coverage range in the flanking regions of these panels was limited. Thus, we developed a MPS system to detect not only the STR core repeat regions, but also variations located in distal positions in the flanking regions. For the system, DNA samples were amplified using two sets of multiplex PCR assay, respectively. The amplification of each STR locus was performed using two pairs of primers, with each primer pair encompassing a wider flanking region on one side (ensuring coverage of the core repeat region). Consequently, the flanking region variations of each STR loci were maximally detectable within the length range compatible with current MPS technology reads. Hence, the alleles observed in the present study covered more flanking regions. The concordance of the system was confirmed by comparing with CE method. The allele frequencies and forensic parameters were calculated in Chinese Han population. Moreover, we statistically analyzed the sequence variations in the flanking regions and evaluated their impacts on forensic application.

Materials and methods

Sample collection and DNA extraction

This study was approved by the Ethical Committee of Fudan University (2020016). Peripheral blood samples were collected using FTA cards from 350 unrelated Chinese Han male individuals. All individuals provided their written informed consent for the collection of blood samples. Genomic DNA was extracted using the BioRobotEZ1 Advanced XL and EZ1 DNA Investigator kits (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA was then quantified using a Qubit 2.0 Fluorometer and a Qubit dsDNA HS Assay Kit (Thermo Fisher).

Selection of STR loci

In this study, twenty-one autosomal STRs (A-STR) were selected (CSF1PO, FGA, TH01, TPOX, vWA, D1S1656, D2S1338, D2S441, D3S1358, D5S818, D6S1043, D7S820, D8S1179, D10S1248, D12S391, D13S317, D16S539, D18S51, D19S433, D21S11 and D22S1045), which were FBI CODIS core STR loci or frequently used in forensic. Besides, twenty-one Y-chromosomal STRs (Y-STR) were selected (DYS19, DYS385a-b, DYS389I-II, DYS390, DYS391, DYS392, DYS437, DYS438, DYS439, DYS444, DYS447, DYS448, DYS456, DYS533, DYS549, DYS570, DYS576, DYS635 and GATA-H4), which were commonly used for the construction of Chinese Y-STR database.

Construction of multiplex PCR systems

In order to enhance the detection of flanking regions surrounding STRs within MPS reads, two pairs of primers were meticulously designed for each STR, ensuring comprehensive coverage of the core region containing the longest known allele. For each pair of primers, one primer binding site was designed in close proximity to the core repeat region of STR, while the other was extended towards the flanking region to encompass the maximum possible length range suitable for MPS reads (considering primer length, sequencing joint, and barcode; typically reaching approximately 200 bp). It is advisable to avoid utilizing the identified SNP within the adjacent region of STR for primer binding. The schematic diagram of primers design was shown in Fig. 1. The primer pair for each locus was designed using Oligo Primer Analysis Software Version 7 (Molecular Biology Insights, Cascade, USA) (https://oligo.net/), and subsequently assessed for potential non-specific hybridizations through the NCBI Basic Local Alignment Search Tool (BLAST) (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The positions and sizes of the amplicons are presented in Table 1A and 1B, while the primer sequences can be found in Supplementary Table S1.

The designed primers were divided into two sets of multiplex PCR systems (pool1 and pool2) for amplification to avoid interaction. The KAPA 2G Fast Multiplex PCR kit (Roche Diagnostics, Rotkreuz, Switzerland) was employed for multiplex amplification. The amplification reaction comprised of 10 µl of KAPA 2G Fast Multiplex PCR Mix, 5 ng of DNA templates, and 1 µl of primer Mix. Amplification was conducted on a GeneAmp™ 9700 PCR System (Thermo Fisher) with the following parameters: initial denaturation at 96℃ for 3 min; followed by 20 cycles of denaturation at 96℃ for 30 s, annealing at 60℃ for 4 min; final extension step at 72℃ for 20 min and hold at 4℃. Amplicons were purified using the Agencourt AMPure XP PCR Purification System (Beckman Coulte, Fullerton, CA, USA) as recommended by the manufacturers.

Fig. 1 Schematic diagram of primers design

Table 1A The amplicon positions and sizes of 21 A-STRs (GRCh38)

Locus	Pool1 amplicon positions	Pool2 amplicon positions	The core repeat region ranges	Position ranges after merge	Sizes	
D1S1656	Chr1: 230,769,537–230,769,840	Chr1: 230,769,446–230,769,722	Chr1: 230,769,616–230,769,683	Chr1: 230,769,446–230,769,840	395	
D2S1338	Chr2: 218,014,813–218,015,107	Chr2: 218,014,707–218,014,980	Chr2: 218,014,859–218,014,950	Chr2: 218,014,707–218,015,107	401	
D2S441	Chr2: 68,011,763–68,012,029	Chr2: 68,011,882–68,012,135	Chr2: 68,011,947–68,011,994	Chr2: 68,011,763–68,012,135	373	
TPOX	Chr2: 1,489,453–1,489,716	Chr2: 1,489,616–1,489,888	Chr2: 1,489,653–1,489,684	Chr2: 1,489,453–1,489,888	436	
D3S1358	Chr3: 45,540,694–45,540,960	Chr3: 45,540,566–45,540,827	Chr3: 45,540,739–45,540,802	Chr3: 45,540,566–45,540,960	395	
FGA	Chr4: 154,587,689–154,587,905	Chr4: 154,587,632–154,587,889	Chr4: 154,587,736–154,587,823	Chr4: 154,587,632–154,587,905	274	
D5S818	Chr5: 123,775,493–123,775,716	Chr5: 123,775,414–123,775,657	Chr5: 123,775,556–123,775,599	Chr5: 123,775,414–123,775,716	303	
CSF1PO	Chr5: 150,076,288–150,076,497	Chr5: 150,076,169–150,076,452	Chr5: 150,076,324–150,076,375	Chr5: 150,076,169–150,076,497	329	
D6S1043	Chr6: 91,740,189–91,740,464	Chr6: 91,740,066–91,740,313	Chr6: 91,740,225–91,740,272	Chr6: 91,740,066–91,740,464	399	
D7S820	Chr7: 84,160,139–84,160,427	Chr7: 84,160,063–84,160,358	Chr7: 84,160,226–84,160,277	Chr7: 84,160,063–84,160,427	365	
D8S1179	Chr8: 124,894,720–124,894,983	Chr8: 124,894,815–124,895,142	Chr8: 124,894,865–124,894,916	Chr8: 124,894,720–124,895,142	423	
D10S1248	Chr10: 129,294,170–129,294,442	Chr10: 129,294,070–129,294,339	Chr10: 129,294,244–129,294,295	Chr10: 129,294,070–129,294,442	373	
TH01	Chr11: 2,170,877–2,171,148	Chr11: 2,171,053–2,171,284	Chr11: 2,171,088–2,171,115	Chr11: 2,170,877–2,171,284	408	
vWA	Chr12: 5,983,936–5,984,169	Chr12: 5,983,868–5,984,090	Chr12: 5,983,977–5,984,044	Chr12: 5,983,868–5,984,169	302	
D12S391	Chr12: 12,296,886–12,297,138	Chr12: 12,296,978–12,297,240	Chr12: 12,297,020–12,297,095	Chr12: 12,296,886–12,297,240	355	
D13S317	Chr13: 82,147,861–82,148,120	Chr13: 82,147,976–82,148,236	Chr13: 82,148,025–82,148,068	Chr13: 82,147,861–82,148,236	376	
D16S539	Chr16: 86,352,546–86,352,802	Chr16: 86,352,659–86,352,940	Chr16: 86,352,702–86,352,745	Chr16: 86,352,546–86,352,940	395	
D18S51	Chr18: 63,281,545–63,281,805	Chr18: 63,281,626–63,281,865	Chr18: 63,281,667–63,281,738	Chr18: 63,281,545–63,281,865	321	
D19S433	Chr19: 29,926,119–29,926,369	Chr19: 29,926,190–29,926,456	Chr19: 29,926,235–29,926,298	Chr19: 29,926,119–29,926,456	338	
D21S11	Chr21: 19,181,889–19,182,143	Chr21: 19,181,935–19,182,169	Chr21: 19,181,973–19,182,099	Chr21: 19,181,889–19,182,169	281	
D22S1045	Chr22: 37,140,090–37,140,379	Chr22: 37,140,225–37,140,511	Chr22: 37,140,287–37,140,337	Chr22: 37,140,090–37,140,511	422	

Table 1B The amplicon positions and sizes of 21 Y-STRs (GRCh38)

Locus	Pool1 amplicon positions	Pool2 amplicon positions	The core repeat region ranges	Position ranges after merge	Sizes	
DYS19	ChrY: 9,684,260–9,684,526	ChrY: 9,684,343–9,684,620	ChrY: 9,684,380–9,684,443	ChrY: 9,684,260–9,684,620	361	
DYS385ab	ChrY: 18,680,462–18,680,734		ChrY: 18,680,632–18,680,687	ChrY: 18,680,462–18,680,734	273	
	ChrY: 18,639,666–18,639,926		ChrY: 18,639,713–18,639,756	ChrY: 18,639,666–18,639,926	261	
DYS389I-II	ChrY: 12,500,371–12,500,647	ChrY: 12,500,413–12,500,663	ChrY: 12,500,448–12,500,611	ChrY: 12,500,371–12,500,663	293	
DYS390	ChrY: 15,163,026–15,163,299	ChrY: 15,162,937–15,163,198	ChrY: 15,163,067–15,163,162	ChrY: 15,162,937–15,163,299	363	
DYS391	ChrY: 11,981,887–11,982,167	ChrY: 11,982,022–11,982,293	ChrY: 11,982,089–11,982,132	ChrY: 11,981,887–11,982,293	407	
DYS392	ChrY: 20,471,946–20,472,229	ChrY: 20,471,826–20,472,088	ChrY: 20,471,987–20,472,025	ChrY: 20,471,826–20,472,229	404	
DYS437	ChrY: 12,346,132–12,346,417	ChrY: 12,346,233–12,346,441	ChrY: 12,346,267–12,346,326	ChrY: 12,346,132–12,346,441	310	
DYS438	ChrY: 12,825,731–12,825,981	ChrY: 12,825,824–12,826,091	ChrY: 12,825,889–12,825,948	ChrY: 12,825,731–12,826,091	361	
DYS439	ChrY: 12,403,359–12,403,609	ChrY: 12,403,458–12,403,705	ChrY: 12,403,517–12,403,564	ChrY: 12,403,359–12,403,705	347	
DYS444	ChrY: 17,114,122–17,114,397	ChrY: 17,114,190–17,114,464	ChrY: 17,114,309–17,114,364	ChrY: 17,114,122–17,114,464	343	
DYS447	ChrY: 13,166,792–13,167,065	ChrY: 13,166,723–13,167,001	ChrY: 13,166,829–13,166,953	ChrY: 13,166,723–13,167,065	343	
DYS448	ChrY: 22,218,851–22,219,114	ChrY: 22,218,893–22,219,160	ChrY: 22,218,923–22,219,078	ChrY: 22,218,851–22,219,160	310	
DYS456	ChrY: 4,402,774–4,403,043	ChrY: 4,402,863–4,403,129	ChrY: 4,402,919–4,402,978	ChrY: 4,402,774–4,403,129	356	
DYS533	ChrY: 16,281,184–16,281,477	ChrY: 16,281,305–16,281,577	ChrY: 16,281,349–16,281,396	ChrY: 16,281,184–16,281,577	394	
DYS549	ChrY: 19,358,155–19,358,448	ChrY: 19,358,284–19,358,557	ChrY: 19,358,338–19,358,389	ChrY: 19,358,155–19,358,557	403	
DYS570	ChrY: 6,993,024–6,993,296	ChrY: 6,993,151–6,993,428	ChrY: 6,993,190–6,993,257	ChrY: 6,993,024–6,993,428	405	
DYS576	ChrY: 7,185,179–7,185,426	ChrY: 7,185,231–7,185,508	ChrY: 7,185,318–7,185,385	ChrY: 7,185,179–7,185,508	330	
DYS635	ChrY: 12,258,716–12,258,990	ChrY: 12,258,814–12,259,041	ChrY: 12,258,860–12,258,951	ChrY: 12,258,716–12,259,041	326	
GATA-H4	ChrY: 16,631,587–16,631,880	ChrY: 16,631,499–16,631,789	ChrY: 16,631,673–16,631,720	ChrY: 16,631,499–16,631,880	382	

Library preparation, normalized and sequencing

The purified amplicons were ligated to adapters with barcodes using the KAPA 2G Fast Multiplex PCR kit for each library. The ligation reaction included 10 µl of KAPA 2G Fast Multiplex PCR Mix, 1 µl of 4 μm TRA-70xR, 1 µl of 4 μm TRA-50xF, and 8 ng of purified amplicons. The enrichment reaction was performed under the following conditions: initial denaturation at 96℃ for 3 min; followed by a total of ten cycles consisting of denaturation at 96℃ for 30 s, annealing at 58℃ for 30 s, extension at 72℃ for 30s; final extension step at 72℃ for 2 min; and holding temperature set to 4℃. Library purification was carried out using the Agencourt AMPure XP PCR Purification System according to the manufacturer’s recommendations. Subsequently, pool1 and pool2 from the same sample were combined.

The quantity of each library was determined using the Qubit 2.0 Fluorometer with the Qubit dsDNA HS Assay Kit. The concentration unit ng/µl was converted to nM using the formula nM = ng/µl / (660 g/mol ×300 bp) ×106 (https://www.science.smith.edu/wp-content/uploads/sites/36/2015/09/Converting-ng-to-nM.pdf). Each library was normalized and pooled to an equimolar concentration of 10 nM, as recommended by the manufacturer. Pooled libraries were sequenced on the MiSeq FGx™ System (Verogen) with Miseq V3 reagent (PE300 strategy), following standard protocol. A total of five sequencing runs were performed, each consisting of 70 samples along with positive and negative controls.

Data analysis

The raw reads were filtered by removing the uncompleted ends and sequencing errors. The reads were then screened and only the ones contain the primer sequences for STRs were used for STR genotyping. A custom Perl script was used to annotate the STRs by directly comparing the sequence in each read with core repeat region for each STR allele. The structure of the STR core repeat region was compiled based on data from the STR Sequencing Project [27] or previous studies’ reports [10–26]. Reads which contain the primer sequences for STRs but can’t annotate to any collected STR allele were used for variation calling. These reads were firstly mapped to human reference genome (GRCh38) using bowtie (version 2-2.2.5) with the “--very-sensitive-local” model [28]. Sequences that differ by up to 1 bp substitution or 1–4 bp insertion/deletion were tolerated in order to get more information of variations. Variations with support of both forward and reverse reads were recorded. The selected reads were split into 5’ flanking, 3’ flanking and core repeat sequences depending on the STR nomenclature recommended by the DNA commission of the International Society for Forensic Genetics (ISFG) [29, 30]. A custom Perl script was used to annotate the sequence variation in each read. Alleles with a minimum of 30× coverage were used for further analysis. The balance threshold of alleles within the locus was set at 60%, and the stutter threshold was uniformly set at 30%. The core repeat sequences from one sample should be consistent between pool1 (5’ flanking) and pool2 (3’ flanking). Thus, alleles from two pools were merged according to core repeat sequences manually. All sequence data were exported and reviewed manually. All alleles identified in the sequencing analysis were then integrated for further analysis.

Capillary electrophoresis (CE) assessing concordance

Ninety-two samples were randomly chosen for the purpose of assessing concordance (The samples with allele dropout due to quality control were excluded). The overlap between STR genotypes generated using MPS and CE methods was assessed by employing two commercially available CE-based STR kits. The GlobalFiler™ PCR Amplification kit (Thermo Fisher) was employed for typing A-STRs, while the Yfiler™ Platinum PCR Amplification kit (Thermo Fisher) was used for typing Y-STRs. These two kits encompassed all 42 STRs except for D6S1043. The ABI 3500XL Genetic Analyzer (Thermo Fisher) was utilized to separate and detect the PCR products in accordance with the manufacturer’s guidelines. The electrophoretic results were subsequently analyzed using GeneMapper® ID-X software v1.4 (Thermo Fisher). To assess any discrepancies in the CE-based typing, we utilized the binary sequence alignment (BAM) file and examined it with the Integrative Genomics Viewer (IGV) [31].

Identification of sequence variations

All merged alleles were integrated following the ISFG recommendations, enabling comparison of these data with the STR Sequencing Project records [27] and previous studies [10–26]. Sanger sequencing was conducted to verify sequence variations not present in the STR Sequencing Project or previous studies, utilizing a BigDye1 Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher).

Forensic parameters and Y-haplotype frequencies

The length and sequence variants per locus were tallied and categorized based on length (length-based, LB), sequence without flanking region (core repeat regions sequence-based, RSB), and sequence with flanking region (core repeat and flanking regions sequence-based, FSB), respectively. For A-STRs, the forensic parameters were calculated using STRAF [32], which encompassed allele count (Nall), genotype count (N), genetic diversity (GD), expected heterozygosity (Hexp), random match probability (RMP), discrimination power (DP), polymorphism information content (PIC), power of exclusion (PE), typical paternity index (TPI) and observed heterozygosity (Hobs). Allele frequencies for LB, RSB, and FSB were calculated in Excel by determining the ratio of the allele count to the total count. HWE was tested using STRAF with a Bonferroni correction [33] applied for multiple comparisons. For Y-STRs, the frequencies of Y-haplotypes were determined using a direct counting method that incorporated LB, RSB, and FSB.

Results

Sequencing performance and quality control

The analysis of 350 samples involved a total of five sequencing runs. A total of 93,185,964 reads were obtained. There was a good balance among the samples, and at least 50,000 reads were obtained for each sample, indicating that the library was pooled well. After removing invalid data (reads with a coverage of less than 30× and reads of stutters) and primer dimers, the average depth of coverage (Doc) of each STR locus was 330×. The average Doc of A-STR was 456×, which was significantly higher than that of Y-STR (191×). The average Doc of pool1 was 313×, and 347× for pool2. For A-STRs, the highest average Doc locus was D5S818 pool1 (1891×) and the lowest was D21S11 pool2 (175×). For Y-STRs, the highest average Doc was DYS391 pool2 (592×), and the lowest was DYS447 pool2 (67×). The Doc of each STR locus was shown in Supplementary Table S2.

Sequence merging and concordance analysis

The sequence merging was performed manually. For A-STRs, all profiles of 350 samples were obtained. The core repeat sequences of each A-STR from pool1 and pool2 of the same sample were compared and confirmed to be consistent. The minimum sequencing depth was higher than 30×. The Y-STRs exhibited a lower depth of coverage compared to the A-STRs. A limited number of Y-STR alleles (DYS19 (6/350), DYS389I-II (2/350), DYS437 (8/350), DYS447 (11/350), DYS448 (16/350), DYS533 (2/350), and GATA-H4 (2/350)) either experienced dropout or had sequencing depths below 30× in single or double pools, rendering the MPS-STR typing for these loci invalid and excluding them from the statistical analysis. Ninety-two random samples were genotyped using two commercial CE-based STR kits and concordance between two methods was confirmed (alleles which were dropout due to quality control were not taken into consideration).

Variations in the flanking regions

A total of 58 variations in the flanking regions (53 SNPs/SNVs and 5 InDels) were observed at 25 loci in 350 samples. The majority of these variations (48/58) were distributed in A-STRs. With the exception of CSF1PO, D3S1358, FGA, and D12S391, variations were observed in the flanking regions. Among these loci, D7S820 exhibited the highest number of variations (seven), while four variations each were observed in the flanking regions of D1S1656, TH01, and TPOX. Other variations (10/58) were distributed in 8 Y-STRs, of which two variations were observed in DYS437 and DYS438, respectively. Among 58 variations in the flanking regions, 7 were not recorded in the Single-Nucleotide Polymorphism database (dbSNP, https://www.ncbi.nlm.nih.gov/snp/). All variations were verified using Sanger sequencing (data not provided) and were listed in Supplementary Table S3.

Diversity of observed alleles

For all 350 samples, all A-STR alleles (14,700) and a total of 7,301 Y-STR alleles were identified (49 Y-STR alleles were excluded due to quality control measures or dropout). The implementation of MPS analysis resulted in a significant enhancement in allele diversity. The magnitude of this enhancement varied across different loci in terms of the number of unique alleles observed.

For the A-STRs, a total of 228 unique LB alleles, 348 unique RSB alleles, and 480 unique FSB alleles were observed. The application of MPS resulted in an increase in the number of identified unique alleles for all A-STRs compared to CE. Notably, twelve loci exhibited increased allele diversity due to variations in both core repeat and flanking region sequences (D1S1656, D2S1338, D2S441, D5S818, D6S1043, D7S820, D8S1179, D13S317, D18S51, D19S433, D21S11, and vWA), while four loci showed increased allele diversity solely due to variations in the core repeat regions (D3S1358, D12S391, FGA, and CSF1PO) and five loci displayed an increase in allele diversity only due to variations in the flanking regions (D10S1248, D16S539, D22S1045, TH01, and TPOX). It was found that D2S1338 and D21S11exhibited the highest levels of genetic diversity with 49 unique alleles each, whereas CSF1PO showed relatively low levels with only eight unique alleles observed. D2S1338 demonstrated the highest growth rate (276.92%), whereas D18S51 exhibited the lowest growth rate (5.88%). The comparison of unique alleles per A-STR locus among LB, RSB, and FSB was conducted, as depicted in Table 2A and Fig. 2A.

Table 2A The number of unique alleles observed at 21 A-STRs by LB, by RSB, and by FSB

Locus	LB	RSB	FSB	Allele increase	Increase (%)	
D2S1338	13	41	49	36	276.92	
D12S391	12	45	45	33	275.00	
D21S11	15	47	49	34	226.67	
D13S317	7	9	19	12	171.43	
D16S433	7	7	19	12	171.43	
D7S820	11	13	31	18	163.64	
vWA	8	20	20	12	150.00	
D8S1179	11	23	27	16	145.45	
D3S1358	8	19	19	11	137.50	
D2S441	10	17	23	13	130.00	
D5S818	8	10	18	10	125.00	
TH01	6	6	12	6	100.00	
TPOX	5	5	10	5	100.00	
D10S1248	8	8	15	7	87.50	
D1S1656	15	19	28	13	86.67	
D19S433	14	17	17	3	21.43	
D6S1043	19	22	22	3	15.79	
FGA	19	22	22	3	15.79	
CSF1PO	7	8	8	1	14.29	
D22S1045	8	8	9	1	12.50	
D18S51	17	18	18	1	5.88	
Total	228	348	480	250	109.65	
Abbreviations LB, length-based alleles. RSB, sequence-based alleles without flanking region. FSB, sequence-based alleles with flanking region

Fig. 2A Allele diversity of A-STRs based on length or sequence (with or without flanking regions)

For the Y-STRs, a total of 148 unique LB alleles, 258 unique RSB alleles, and 271 unique FSB alleles were observed. The increase in diversity for Y-STRs was comparatively smaller than that observed for the A-STRs. Approximately 61.9% of Y-STRs (13/21) exhibited an augmented allelic diversity. Among these loci, seven Y-STRs demonstrated enhanced allele diversity due to variations in both core repeat and flanking region sequences (DYS385ab, DYS390, DYS437, DYS438, DYS447, and GATA-H4). Four Y-STRs (DYS389II, DYS392, DYS448, and DYS635) displayed an increase in allele diversity exclusively owing to differences in core repeat region sequences, whereas two Y-STRs (DYS391 and DYS570) showed enhanced diversity solely due to variations in the flanking regions. For the remaining eight Y-STR loci (DYS19, DYS389I, DYS439, DYS444, DYS456, DYS533, DYS549, and DYS576), no variation was detected either in the core repeat or flanking regions indicating a lack of increase in allele diversity. Notably, among all Y-STRs, DYS447 exhibited not only the highest level of diversity with 53 unique alleles but also showcased a remarkable growth rate of 562.50%. The comparison of unique alleles per Y-STR locus among LB, RSB, and FSB was conducted, as depicted in Table 2B and Figure 2B.

Fig. 2B Allele diversity of Y-STRs based on length or sequence (with or without flanking regions)

Table 2B The number of unique alleles observed at 21 Y-STRs by LB, by RSB, and by FSB

Locus	LB	RSB	FSB	Allele increase	Increase (%)	
DYS447	8	52	53	45	562.50	
DYS389II	8	34	34	26	325.00	
DYS437	4	8	10	6	150.00	
DYS448	8	19	19	11	137.50	
DYS390	9	19	21	12	133.00	
DYS635	8	17	17	9	112.50	
GATA-H4	4	5	8	4	100.00	
DYS438	5	6	8	3	60.00	
DYS385ab	20	23	24	4	20.00	
DYS391	5	5	6	1	20.00	
DYS392	9	10	10	1	11.11	
DYS570	10	10	11	1	10.00	
DYS19	6	6	6	0	0	
DYS389I	5	5	5	0	0	
DYS439	5	5	5	0	0	
DYS444	6	6	6	0	0	
DYS456	6	6	6	0	0	
DYS533	7	7	7	0	0	
DYS549	5	5	5	0	0	
DYS576	10	10	10	0	0	
Total	148	258	271	123	83.11	
Abbreviations LB, length-based alleles. RSB, sequence-based alleles without flanking region. FSB, sequence-based alleles with flanking region

Allele frequencies and forensic parameters

The frequencies of LB, RSB, and FSB alleles were determined for all STR loci in 350 individuals using the counting method. The results are summarized in Supplementary Tables S4 and S5. Following Bonferroni correction (α = 0.05/21), all A-STRs allele data demonstrated adherence to HWE expectations, as indicated in Supplementary Table S6. Therefore, the population data obtained from this study can be considered representative.

The A-STR loci were analyzed using STRAF software, and the forensic parameters (as described in the Materials and Methods section) are presented in Supplementary Table S6. The average GD/Hexp values for A-STRs, when analyzed based on length, were 0.7911; however, when assessed based on the repeat region sequence and accounting for sequence variation in the flanking regions, they increased to 0.8106 and 0.8346, respectively. The combined RMP for the 21 A-STRs were calculated as 6.42 × 10–25, 4.45 × 10–27, and 2.48 × 10–29 for LB, RSB, and FSB, respectively. By solely incorporating sequence variations in the repeat regions, the combined RMP decreased significantly by more than a hundredfold compared to length-based alleles alone. Furthermore, inclusion of sequence variations in both repeat and flanking regions enabled MPS analysis to reduce the combined RMP drastically by over 2.5887 × 104 times compared to CE method implementation. The observed trends were consistent across other forensic parameters resulting from a significant increase in the availability of unique alleles.

A total of 340 distinct Y-STR haplotypes were observed in 350 individuals. Despite an increase in allele diversity, sequence variations did not contribute to the augmentation of unique haplotypes. Out of the 340 unique Y-STR haplotypes, one-time occurrence was noted for 333 haplotypes, twice for 5 haplotypes, thrice for 1 haplotype, and four times for another single haplotype. The random match probability (RMP) was calculated as 0.0031, while the discrimination power (DP) and heterozygosity coefficient (HD) were determined as 0.9969 and 0.9998 respectively.

Discussion

Over the past decade, there has been significant advancement in sequencing technology. Various techniques, including clonal nucleic acid amplification followed by sequencing by synthesis (SBS) [34], nanopore sequencing [35], and real-time single-molecule sequencing [36], have laid the groundwork for novel sequencing approaches. Among these, the advantages of MPS over conventional CE methods have positioned it as a focal point in forensic genetics [1]. MPS-based STR genotyping research has been performed in a large number of previous studies, in which the flanking region variations have been mentioned several times. Silva et al. [21] analyzed MPS-STR data of 59 individuals from the Brazilian population by using the PowerSeq™ Auto System and 17 flanking region variations were observed, of which 16 were located in the flanking regions of 9 A-STRs and another was in Y-STR. Kim et al. [19] reported 15 variations in the flanking regions of 8 A-STR in a Korean population involving 250 individuals. Gettings et al. [14] used the PowerSeq™ Auto System to perform MPS-STR studies on 183 individuals from different ethnic groups and summarized all polymorphisms within 500 bp upstream and downstream of commonly used STR in forensic genetics in the 1000 Genomes Phase 3 database. It was demonstrated that a large number of population genetic data involving STR flanking region variations was needed to support MPS forensic application. Herein, we have developed this MPS system to systematically investigate the flanking polymorphisms of common STRs.

Currently, the majority of MPS-STR studies have utilized commercially available MPS-STR panels, with the ForenSeq™ DNA Signature Prep Kit being widely acknowledged as the most frequently employed panel in MPS-STR analysis. It has consistently demonstrated remarkable efficacy in numerous studies. With the utilization of this commercial MPS kit, Delest et al. [24] conducted a comprehensive MPS-STR investigation in a French population consisting of 169 individuals. Similarly, Khubrani et al. [23] performed an extensive MPS-STR analysis on the Saudi Arabian population, while Wendt et al. [18] carried out an insightful MPS-STR study on Yavapai Native Americans. Precision ID GlobalFiler™ NGS STR Panel v2 is another widely used commercial MPS-STR panel. Dash et al. [26] employed this panel to present a comprehensive investigation on sequence variations, flanking region variations, and allele frequencies at 31 STRs within the Indian population. Similarly, Wang et al. [25] conducted an investigation encompassing Chinese Tibetan and Han populations with Precision ID GlobalFiler™ NGS STR Panel v2, while Barrio et al. [22] presented a study focused on MPS data of 31 STRs from 496 Spanish individuals. In terms of loci overlap, ForenSeq™ DNA Signature Prep Kit encompasses 41 STRs (except for DYS447) investigated in this study, whereas Precision ID GlobalFiler™ NGS STR Panel v2 includes all A-STRs examined. Compared with previous studies, the emphasis of our study was primarily on the detection of variations in the flanking regions. With the premise of ensuring that the core region was detected, two primer pairs per STR were designed to maximize the detectability of flanking region variations within the length range compatible with current MPS technology reads. Among the 58 variations observed in the flanking regions in our study, 37 were located sufficiently distal from the core repeat region to remain undetected by using ForenSeq™ DNA Signature Prep Kit, despite successful detection of STR core repeat region and proximal flanking variations. Although the detected range of Precision ID GlobalFiler™ NGS STR Panel v2 is greater than ForenSeq™ DNA Signature Prep Kit (SE400 > PE300), it should be noted that among the 48 observed variations in the flanking regions of A-STRs, 23 were located outside of the detectable range within the overlapped loci. The detail was shown in Supplementary Table S3. By contrast, all reported variations in the flanking regions of overlapped loci from previous studies above fell within the range of our developed MPS system (listed in Table 1), although certain variants were not detected in this study due to population disparities or their low frequency.

Utilizing MPS to analyze STR can not only distinguish different alleles with the same length, but also identify the variations in the core repeat regions and flanking regions. Integration of the core repeat regions with adjacent flanking regions variations enables the formation of alleles that serve as compound markers, encompassing STR-SNP/SNV/InDel variations. The MPS-STR alleles can significantly enhance the performance of STR in forensic applications by increasing allelic diversity, aiding kinship interpretation, and facilitating mixed sample separation. In this study, the diversity of observed alleles based on LB, RSB, and FSB indicated that the sequence variants had a greater impact on A-STRs and could clearly increase MPS-STR genotyping’s ability to identify individuals. The findings were in line with the reports of previous studies [10–26]. However, our study revealed a higher diversity of observed alleles based on FSB compared to previous studies, primarily due to enhanced detection of variations in the flanking regions. In contrast, it appears that the impact of sequence variants on Y-STR haplotype typing was comparatively diminished in this study. Comparing the MPS and CE method, there was no increase in the number of unique Y-STR haplotypes despite of the increase Y-STR allelic diversity. Except for the SNP in the flanking region of GATA-H4 (no rs, position: ChrY: 16631756, frequency 68/350), the variations of the detected Y-STR flanking regions were generally low in the population of this study, which did not improve the ability to identify Y-STR haplotypes.

The CE method currently represents the gold standard in forensic genetics [37]. Prior to implementing MPS in forensic casework, it is imperative to ensure the compatibility of MPS data with existing CE-based forensic databases. Therefore, it is crucial to evaluate the concordance between MPS and CE. The presence of variations in the flanking regions not only poses challenges to STR genotype analysis but also contributes to occasional discordances. Although we have confirmed the concordance between our MPS system and CE method through a sample set of ninety-two randomly selected individuals in this study, sporadic discrepancies have been previously reported [12, 13]. In some loci, the variations in the adjacent flanking regions may cause the flanking sequence being structurally the same as the core repeat motifs. For example, in D13S317, the core repeat motif is TATC and the reference sequence (RefSeq) of 3’ flanking region adjacent to the core repeat region is AATCAATC. Two SNP (rs9546005 and rs202043589) are frequently observed so that 3’ flanking region sequence is TATCAATC (rs9546005 was underlined) or TATCTATC (rs202043589 was underlined). The one or two additional repeats are not involved by length-based CE typing, but may be counted by MPS if the bioinformatic algorithms only focus on the core repeat motif. In the East Asia (EAS) population from the 1000 Genomes Phase 3 dataset, the frequency of the rs9546005 T allele is 48.5%, and the frequency of the rs202043589 T allele is 5.9%, whereas the frequencies of rs9546005 and rs202043589 were 50.9% (356/700) and 6.4% (45/700), respectively, in this study. Analogously, another example in D5S818, the 5’ flanking region contains CTCT adjacent to the core repeat motif (ATCT). The variant A allele of rs73801920 can construct an additional ATCT repeat which may be counted by sequence-based typing, whereas would not be counted by CE typing. The 1000 Genomes does not include rs73801920 frequency, and in this study, the frequency of the A allele was 17.6% (123/700). Such potential discordances have been resolved by improvements in bioinformatics analysis. In addition, there were other three scenarios that could cause discordance between MPS and CE as Gettings et al. [11] summarized: ① InDels in the flanking region: a discordance would occur if an InDel was located inside the CE amplified region but outside the MPS bioinformatic recognition sites. ② Bioinformatic null allele I: one allele would drop out in the bioinformatic pipeline due to variants in the same region as a bioinformatic recognition site, which led to the incorrect appearance of a homozygote. ③ Bioinformatic null allele II: there was not a bin in the bioinformatic configuration file that matched the observed allele. The challenges presented in Scenarios ② and ③ can be addressed through advancements in bioinformatics analysis, as discussed earlier. However, Scenario ① remains unresolved due to the current limitations of MPS read length. In this study, Among the 58 variations in the flanking regions observed, five were flanking region InDels. Especially in D18S51 and D19S433, the variations observed were all InDels. Because these InDels were both within the detection range of MPS and CE, no discordance was observed. In summary, it is very helpful for forensic researchers to understand the flanking region polymorphisms in STR analysis, since discordances between MPS and CE are mainly caused by the variations in the flanking regions.

The detectable lengths of the flanking regions within the range of MPS reads primarily depend on the lengths of the core repeat regions, given that the overall length detected by MPS remains constant. The lengths of the core repeat regions of STRs vary from locus to locus. For locus with relatively short core repeat regions, such as TPOX and TH01, the range of the flanking regions that can be detected is large. The sequence variation in TPOX has rarely been reported in previous studies. In a large population study, Novroski et al. [16, 17] using the MiSeq FGx analyzed 777 unrelated individuals from four major population groups (US Caucasian n = 210, African American n = 200, Hispanic n = 198, and East Asian n = 169). In 27 A-STRs, Only TPOX did not have any sequence variation to be observed. It is indicated that few sequence variations are in the core repeat region or adjacent flanking regions of TPOX. In our study, a total of four variations were observed in the distal flanking region of TPOX (rs149212737, rs115970091, rs1449872726, rs13413321), in which the frequency of rs13413321 was more than half (368/700). Similarly, four variations (rs535300047, rs1472955972, rs1279211197, and rs369097987) were observed in the distal flanking region of TH01 which was rarely reported with sequence variations as well. By contrast, for locus with relatively long core repeat regions, such as FGA and D21S11, the range of the flanking regions that can be detected is limited. In our study, no variation was found in the flanking region of FGA, and only one variation (rs1051967683) was observed in D21S11.

The 58 flanking region variations observed in this study could be divided into three categories: ① The variations with high polymorphism, such as rs9546005 and rs202043589 of D13S317, rs58390469 and rs79534691 of D2S441, rs1728369 and rs11642858 of D16S539. This category of variations has a great impact on MPS-STR typing and can significantly improve the identification ability of STRs, which are reflected in the forensic parameters (Supplementary Table S6). ② The variations with low frequency (< 1%), such as rs1336239361 of D2S1338 and rs1030964212 of D8S1179. The increased number of alleles with this kind of flanking region variations has a negligible effect on the observed increase in heterozygosity. Although this category of variations is of limited help in improving the discriminative power of MPS-STR typing, it may play an important role in some cases such as separating mixture samples. ③ The variations in linkage disequilibrium, such as rs75219269, rs11063969, and rs11063971 of vWA. The three SNPs exhibit linkage with each other, despite the relatively high frequency of the mutant type (130/700). Furthermore, these SNPs were consistently observed within the same allele, vWA [CE14]-GRCh38-Chr12-5983868-5984169 [TAGA]3[TGGA][TAGA]3[CAGA]4[TAGA][CAGA][TAGA] 5,983,970-G; 5,984,116-T; 5,984,121-T; 5,984,134-C, and the sequence variations were also detected in the core repeat regions (Supplementary Table S4). This category of variations is essentially equivalent to only one variation.

Conclusion

The effectiveness of MPS as a supplementary tool to the conventional CE method in forensic routine work has been consistently demonstrated by numerous studies. However, there are still a few pending tasks that need to be accomplished prior to its complete implementation. In this study, we presented a multiplex MPS system designed to selectively amplify the flanking regions of STRs, aiming to investigate the potential variations in these regions. The concordance and characterization of MPS were conducted on 21 A-STRs and 21 Y-STRs in a Chinese Han population. The compatibility between MPS and CE methods was reaffirmed. A total of 58 variations, including 53 SNPs/SNVs and 5 InDels, were observed in the flanking regions. Comparisons with previous studies primarily focus on the detection range of our developed system in the overlapped STR loci. Furthermore, the analysis and comparison of allele diversity, allele frequencies, and forensic parameters per locus by LB, RSB, and FSB effectively highlighted the advantages of MPS as well as the significance of polymorphisms in flanking regions.

In conclusion, revealing additional sequence variations in the core repeat and flanking regions of STRs can yield numerous advantages beyond a mere enhancement in discrimination power when utilizing these markers for direct matching or relationship calculations. The identification of variations in the flanking regions can play important rule in both MPS and CE method. For CE, it is advantageous to avoid the presence of polymorphic primer binding sites during assay design, to elucidate the underlying reasons for discordance observed among different primer sets amplifying identical STR loci, and to comprehend the origin of alleles exhibiting atypical sizes. In the case of MPS, it proves beneficial in enhancing allelic diversity, facilitating kinship interpretation, and separating mixture samples, as well as refining both amplification primer design and bioinformatic algorithm development. This study delved deeper into the genetic information of forensic commonly used STR and advanced the application of massively parallel sequencing in forensic genetics.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The authors wish to thank all the volunteers who provided the samples used in this study.

Author contributions

H.L. and J.X. designed the experiments. H.L. and B.L. were major contributors in conducting experiments. B.L. collected samples. Y.L., F.Y., and Y.C. performed DNA extraction, library preparation, normalized, and sequencing. X.L. performed data analysis. H.L. wrote the manuscript. C.L. and Z.Z. revised the manuscript. All authors reviewed and approved the submitted version of the manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (81930056 and 82293654), and the Public Interest Research Grant Programs of National Research Institutes (Grant No. GY2024G-3). The funders had no role in study design, data analysis, or manuscript preparation.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethical Committee of Fudan University (2020016). All individuals provided their written informed consent for the collection of blood samples. All the experimental procedures were performed by the standards of the Declaration of Helsinki 1964.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

MPS Massively parallel sequencing

NGS Next-generation sequencing

STRs Short tandem repeats

CE Capillary electrophoresis

A-STRs Autosomal STRs

Y-STRs Y-chromosomal STR

LB Length-based

RSB Core repeat regions sequence-based (sequence without flanking region)

FSB Core repeat and flanking regions sequence-based (sequence with flanking region)

Hexp Expected heterozygosity

Hobs Heterozygosity

GD Genetic diversity

PIC Polymorphism information content

RMP Random match probability

DP Discrimination power

PE Power of exclusion

TPI Typical paternity index

HWE Hardy–Weinberg equilibrium

HD Haplotype diversity

Doc Depth of coverage

SBS Sequencing by synthesis

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hui Li and Beixu Li contributed equally to this work.
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