Identifying novel microhaplotypes for ancestry inference
Tóm tắt
The use of DNA to determine the ancestry of an individual is becoming more and more important in the areas of forensics. Kidd et al. (Forensic Sci Int Genet 12:215–224, 2014) have been the first to identify and catalog haplotypes, termed as minihaplotypes (1–10-kilobase spans) and microhaplotypes (≤ 200 bp), with potential use in forensic analysis. In the present study, we selected 10 short ancestry informative microhaplotypes by calculating the informativeness (In) according to Rosenberg et al. (Am J Hum Genet 73(6):1402–1422, 2003). In total, 2504 individuals from 26 populations in 1000 Genomes Project database Phase 3 were enrolled. Among the studied microhaplotypes, eight of them are comprised of 3 SNPs while two microhaplotypes are made up of 4 SNPs. The size (molecular extent) range of 10 microhaplotypes is 5 to 48 bp with an average of 31.4 bp. The heterozygosity value ranges from 0.2235 to 0.8958 with an average of 0.6593. The average power of discrimination (PD) values is 0.7944 and ranges from 0.3786 to 0.9242. Analyses of this dataset provided clear differentiation of the populations from the Africa, East Asia, South Asia, and Europe biogeographic regions. However, individuals from American ancestry were not well separated. To conclude, our results revealed the significance of using microhaplotypes as an ancestry informative marker. The present panel could offer a valid complementary tool in forensic applications.
Tài liệu tham khảo
Phillips C (2015) Forensic genetic analysis of bio-geographical ancestry. Forensic Sci Int Genet 18:49–65. https://doi.org/10.1016/j.fsigen.2015.05.012
Kidd JR, Friedlaender FR, Speed WC, Pakstis AJ, De La Vega FM, Kidd KK (2011) Analyses of a set of 128 ancestry informative single-nucleotide polymorphisms in a global set of 119 population samples. Investig Genet 2(1):1. https://doi.org/10.1186/2041-2223-2-1
Phillips C, Parson W, Lundsberg B, Santos C, Freire-Aradas A, Torres M, Eduardoff M, Borsting C, Johansen P, Fondevila M, Morling N, Schneider P, Carracedo A, Lareu MV (2014) Building a forensic ancestry panel from the ground up: the EUROFORGEN Global AIM-SNP set. Forensic Sci Int Genet 11:13–25. https://doi.org/10.1016/j.fsigen.2014.02.012
Jiang L, Wei YL, Zhao L, Li N, Liu T, Liu HB, Ren LJ, Li JL, Hao HF, Li Q, Li CX (2018) Global analysis of population stratification using a smart panel of 27 continental ancestry-informative SNPs. Forensic Sci Int Genet 35:e10–e12. https://doi.org/10.1016/j.fsigen.2018.05.006
Elhaik E, Tatarinova T, Chebotarev D, Piras IS, Maria Calo C, De Montis A, Atzori M, Marini M, Tofanelli S, Francalacci P, Pagani L, Tyler-Smith C, Xue Y, Cucca F, Schurr TG, Gaieski JB, Melendez C, Vilar MG, Owings AC, Gomez R, Fujita R, Santos FR, Comas D, Balanovsky O, Balanovska E, Zalloua P, Soodyall H, Pitchappan R, Ganeshprasad A, Hammer M, Matisoo-Smith L, Wells RS (2014) Geographic population structure analysis of worldwide human populations infers their biogeographical origins. Nat Commun 5:3513. https://doi.org/10.1038/ncomms4513
Qin P, Li Z, Jin W, Lu D, Lou H, Shen J, Jin L, Shi Y, Xu S (2014) A panel of ancestry informative markers to estimate and correct potential effects of population stratification in Han Chinese. Eur J Hum Genet 22(2):248–253. https://doi.org/10.1038/ejhg.2013.111
Soundararajan U, Yun L, Shi M, Kidd KK (2016) Minimal SNP overlap among multiple panels of ancestry informative markers argues for more international collaboration. Forensic Sci Int Genet 23:25–32. https://doi.org/10.1016/j.fsigen.2016.01.013
Li CX, Pakstis AJ, Jiang L, Wei YL, Sun QF, Wu H, Bulbul O, Wang P, Kang LL, Kidd JR, Kidd KK (2016) A panel of 74 AISNPs: improved ancestry inference within Eastern Asia. Forensic Sci Int Genet 23:101–110. https://doi.org/10.1016/j.fsigen.2016.04.002
Pakstis AJ, Haigh E, Cherni L, ElGaaied ABA, Barton A, Evsanaa B, Togtokh A, Brissenden J, Roscoe J, Bulbul O, Filoglu G, Gurkan C, Meiklejohn KA, Robertson JM, Li CX, Wei YL, Li H, Soundararajan U, Rajeevan H, Kidd JR, Kidd KK (2015) 52 additional reference population samples for the 55 AISNP panel. Forensic Sci Int Genet 19:269–271. https://doi.org/10.1016/j.fsigen.2015.08.003
Kosoy R, Nassir R, Tian C, White PA, Butler LM, Silva G, Kittles R, Alarcon-Riquelme ME, Gregersen PK, Belmont JW, De La Vega FM, Seldin MF (2009) Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America. Hum Mutat 30(1):69–78. https://doi.org/10.1002/humu.20822
Chen P, Yin C, Li Z, Pu Y, Yu Y, Zhao P, Chen D, Liang W, Zhang L, Chen F (2018) Evaluation of the microhaplotypes panel for DNA mixture analyses. Forensic Sci Int Genet 35:149–155. https://doi.org/10.1016/j.fsigen.2018.05.003
Phillips C, Fernandez-Formoso L, Gelabert-Besada M, Garcia-Magarinos M, Santos C, Fondevila M, Carracedo A, Lareu MV (2013) Development of a novel forensic STR multiplex for ancestry analysis and extended identity testing. Electrophoresis 34(8):1151–1162. https://doi.org/10.1002/elps.201200621
Pakstis AJ, Fang R, Furtado MR, Kidd JR, Kidd KK (2012) Mini-haplotypes as lineage informative SNPs and ancestry inference SNPs. Eur J Hum Genet 20(11):1148–1154. https://doi.org/10.1038/ejhg.2012.69
Kidd KK, Pakstis AJ, Speed WC, Lagace R, Chang J, Wootton S, Haigh E, Kidd JR (2014) Current sequencing technology makes microhaplotypes a powerful new type of genetic marker for forensics. Forensic Sci Int Genet 12:215–224. https://doi.org/10.1016/j.fsigen.2014.06.014
Kidd KK, Speed WC (2015) Criteria for selecting microhaplotypes: mixture detection and deconvolution. Investig Genet 6(1):1. https://doi.org/10.1186/s13323-014-0018-3
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73(6):1402–1422. https://doi.org/10.1086/380416
Rosenberg NA (2005) Algorithms for selecting informative marker panels for population assignment. J Comput Biol 12(9):1183–1201. https://doi.org/10.1089/cmb.2005.12.1183
Kidd KK (2016) Proposed nomenclature for microhaplotypes. Hum Genomics 10(1):16. https://doi.org/10.1186/s40246-016-0078-y
Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research--an update. Bioinformatics (Oxford, England) 28(19):2537–2539. https://doi.org/10.1093/bioinformatics/bts460
Gower JC (2005) Principal coordinates analysis. Wiley
Gao Z, Chen X, Zhao Y, Zhao X, Zhang S, Yang Y, Wang Y, Zhang J (2018) Forensic genetic informativeness of an SNP panel consisting of 19 multi-allelic SNPs. Forensic Sci Int Genet 34:49–56. https://doi.org/10.1016/j.fsigen.2018.01.006