scholarly journals Comparison of Two Massively Parallel Sequencing Platforms using 83 Single Nucleotide Polymorphisms for Human Identification

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Dame Loveliness T. Apaga ◽  
Sheila E. Dennis ◽  
Jazelyn M. Salvador ◽  
Gayvelline C. Calacal ◽  
Maria Corazon A. De Ungria
Author(s):  
Ashley M. Cooley ◽  
Kelly A. Meiklejohn ◽  
Natalie Damaso ◽  
James M. Robertson ◽  
Tracey Dawson Cruz

Thermo Fisher Scientific released the Precision ID Ancestry Panel, a 165-single-nucleotide polymorphism (SNP) panel for ancestry prediction that was initially compatible with the manufacturer’s massively parallel sequencer, the Ion Torrent Personal Genome Machine (PGM). The semiautomated workflow using the panel with the PGM involved several time-consuming manual steps across three instruments, including making templating solutions and loading sequencing chips. In 2014, the manufacturer released the Ion Chef robot, followed by the Ion S5 massively parallel sequencer in late 2015. The robot performs the templating with reagent cartridges and loads the chips, thus creating a fully automated workflow across two instruments. The objective of the work reported here is to compare the performance of two massively parallel sequencing systems and ascertain if the change in the workflow produces different ancestry predictions. For performance comparison of the two systems, forensic-type samples ( n = 16) were used to make libraries. Libraries were templated either with the Ion OneTouch 2 system (for the PGM) or on the Ion Chef robot (for the S5). Sequencing results indicated that the ion sphere particle performance metrics were similar for the two systems. The total coverages per SNP and SNP quality were both higher for the S5 system. Ancestry predictions were concordant for the mock forensic-type samples sequenced on both massively parallel sequencing systems. The results indicated that automating the workflow with the Ion Chef system reduced the labor involved and increased the sequencing quality.


2016 ◽  
Author(s):  
Mark Barash ◽  
Philipp E. Bayer ◽  
Angela van Daal

AbstractDespite intensive research on genetics of the craniofacial morphology using animal models and human craniofacial syndromes, the genetic variation that underpins normal human facial appearance is still largely elusive. Recent development of novel digital methods for capturing the complexity of craniofacial morphology in conjunction with high-throughput genotyping methods, show great promise for unravelling the genetic basis of such a complex trait.As a part of our efforts on detecting genomic variants affecting normal craniofacial appearance, we have implemented a candidate gene approach by selecting 1,201 single nucleotide polymorphisms (SNPs) and 4,732 tag SNPs in over 170 candidate genes and intergenic regions. We used 3-dimentional (3D) facial scans and direct cranial measurements of 587 volunteers to calculate 104 craniofacial phenotypes. Following genotyping by massively parallel sequencing, genetic associations between 2,332 genetic markers and 104 craniofacial phenotypes were tested.An application of a Bonferroni–corrected genome–wide significance threshold produced significant associations between five craniofacial traits and six SNPs. Specifically, associations of nasal width with rs8035124 (15q26.1), cephalic index with rs16830498 (2q23.3), nasal index with rs37369 (5q13.2), transverse nasal prominence angle with rs59037879 (10p11.23) and rs10512572 (17q24.3), and principal component explaining 73.3% of all the craniofacial phenotypes, with rs37369 (5p13.2) and rs390345 (14q31.3) were observed.Due to over-conservative nature of the Bonferroni correction, we also report all the associations that reached the traditional genome-wide p-value threshold (<5.00E-08) as suggestive. Based on the genome-wide threshold, 8 craniofacial phenotypes demonstrated significant associations with 34 intergenic and extragenic SNPs. The majority of associations are novel, except PAX3 and COL11A1 genes, which were previously reported to affect normal craniofacial variation.This study identified the largest number of genetic variants associated with normal variation of craniofacial morphology to date by using a candidate gene approach, including confirmation of the two previously reported genes. These results enhance our understanding of the genetics that determines normal variation in craniofacial morphology and will be of particular value in medical and forensic fields.Author SummaryThere is a remarkable variety of human facial appearances, almost exclusively the result of genetic differences, as exemplified by the striking resemblance of identical twins. However, the genes and specific genetic variants that affect the size and shape of the cranium and the soft facial tissue features are largely unknown. Numerous studies on animal models and human craniofacial disorders have identified a large number of genes, which may regulate normal craniofacial embryonic development.In this study we implemented a targeted candidate gene approach to select more than 1,200 polymorphisms in over 170 genes that are likely to be involved in craniofacial development and morphology. These markers were genotyped in 587 DNA samples using massively parallel sequencing and analysed for association with 104 traits generated from 3-dimensional facial images and direct craniofacial measurements. Genetic associations (p-values<5.00E-08) were observed between 8 craniofacial traits and 34 single nucleotide polymorphisms (SNPs), including two previously described genes and 26 novel candidate genes and intergenic regions. This comprehensive candidate gene study has uncovered the largest number of novel genetic variants affecting normal facial appearance to date. These results will appreciably extend our understanding of the normal and abnormal embryonic development and impact our ability to predict the appearance of an individual from a DNA sample in forensic criminal investigations and missing person cases.


PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0138259 ◽  
Author(s):  
Pınar Kavak ◽  
Bayram Yüksel ◽  
Soner Aksu ◽  
M. Oguzhan Kulekci ◽  
Tunga Güngör ◽  
...  

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Maura Costello ◽  
Mark Fleharty ◽  
Justin Abreu ◽  
Yossi Farjoun ◽  
Steven Ferriera ◽  
...  

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