Impact of SNP microarray analysis of compromised DNA on kinship classification success in the context of investigative genetic genealogy

Vries, Jard H. de; Kling, Daniel; Vidaki, Athina; Arp, Pascal; Kalamara, Vivian; Verbiest, Michaël; Piniewska-Róg, Danuta; Parsons, Thomas J.; Uitterlinden, André G.; Kayser, Manfred

doi:10.1016/j.fsigen.2021.102625

Cited by 41 publications

(22 citation statements)

References 47 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, the samples are often degraded, and in some cases, DNA is present in only minute, trace amounts. The combination of low input together with degraded template DNA can lead to both increased missingness (low call rate for microarrays or high dropout rate for sequencing) and an increased genotyping error ( Alaeddini et al, 2010 ; Loreille et al, 2011 ; de Vries et al, 2022 ). All the methods discussed in this work were developed and optimized for contemporary genotyping array data or variant calls from short-read whole-genome sequence data, both of which have extremely high accuracy and low missing data rates, given high coverage and high-quality samples.…”

Section: Discussionmentioning

confidence: 99%

“…The goal of this study was to evaluate the accuracy of genome-wide relatedness methods and IBD segment approaches in the presence of challenges that are commonly encountered with forensic data, namely, the high level of dropout (low call rate) and increased genotyping error. This study differs from Gorden et al (2022 ), where we simulate genome-wide microarray rather than SNP capture; this differs from similar work in de Vries et al (2022 ) by using different simulation strategies, different pedigree and error structures, and uses both crossover interference and a sex-specific genetic map for forward-time simulation. The challenges with forensic samples are commonly encountered due to samples being degraded, contaminated, and/or having limited input from trace amounts of DNA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Impact of Dropout and Genotyping Error on SNP-Based Kinship Analysis With Forensic Samples

et al. 2022

View full text Add to dashboard Cite

Technological advances in sequencing and single nucleotide polymorphism (SNP) genotyping microarray technology have facilitated advances in forensic analysis beyond short tandem repeat (STR) profiling, enabling the identification of unknown DNA samples and distant relationships. Forensic genetic genealogy (FGG) has facilitated the identification of distant relatives of both unidentified remains and unknown donors of crime scene DNA, invigorating the use of biological samples to resolve open cases. Forensic samples are often degraded or contain only trace amounts of DNA. In this study, the accuracy of genome-wide relatedness methods and identity by descent (IBD) segment approaches was evaluated in the presence of challenges commonly encountered with forensic data: missing data and genotyping error. Pedigree whole-genome simulations were used to estimate the genotypes of thousands of individuals with known relationships using multiple populations with different biogeographic ancestral origins. Simulations were also performed with varying error rates and types. Using these data, the performance of different methods for quantifying relatedness was benchmarked across these scenarios. When the genotyping error was low (<1%), IBD segment methods outperformed genome-wide relatedness methods for close relationships and are more accurate at distant relationship inference. However, with an increasing genotyping error (1–5%), methods that do not rely on IBD segment detection are more robust and outperform IBD segment methods. The reduced call rate had little impact on either class of methods. These results have implications for the use of dense SNP data in forensic genomics for distant kinship analysis and FGG, especially when the sample quality is low.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Dropout and Genotyping Error on SNP-Based Kinship Analysis With Forensic Samples

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The accuracy of estimating the degrees of the relationships obtained from the real data was much lower than the simulated data, which indicated that the simulation model used in Ped-sim might not precisely reflect the genotyping errors in the real data, and thus may result in overfitting in the training dataset. Better genotyping error models ( de Vries et al, 2022 ; Nagraj et al, 2022 ) need to be developed to simulate SNP profiles that better approximate real SNP profiles generated from low-quality samples. However, in the simulations, the most important issue may be “what error rate should we assign to each type of error defined in the model?” As far as we know, limited studies have been done to generate empirical data for estimating the error rates (or the range of the error rates) for different types of genotype errors, nor which model best fits the real WGS data generated from the missing persons samples.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach for missing persons cases with high genotyping errors

Huang

Liu

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship.ip estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

show abstract

“…If a population geneticist today wished to study the ancestry of an unknown individual sample, they would resort to genomewide analysis via a chip typing hundreds of thousands of SNPs, or even whole-genome sequencing. But forensic scientists do not usually have this luxury, since the amount and quality of DNA available is often low [ 32 , 33 ]. Furthermore, the need for methods to be forensically validated, acceptable in the courtroom and compatible with existing investigative databases limits the application of genomewide techniques, and the number and type of markers that can be studied.…”

Section: No-suspect Cases: Dna-based Intelligence On Ancestrymentioning

confidence: 99%

Forensic genetics through the lens of Lewontin: population structure, ancestry and race

Jobling

2022

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

In his famous 1972 paper, Richard Lewontin used ‘classical’ protein-based markers to show that greater than 85% of human genetic diversity was contained within, rather than between, populations. At that time, these same markers also formed the basis of forensic technology aiming to identify individuals. This review describes the evolution of forensic genetic methods into DNA profiling, and how the field has accounted for the apportionment of genetic diversity in considering the weight of forensic evidence. When investigative databases fail to provide a match to a crime-scene profile, specific markers can be used to seek intelligence about a suspect: these include inferences on population of origin (biogeographic ancestry) and externally visible characteristics, chiefly pigmentation of skin, hair and eyes. In this endeavour, ancestry and phenotypic variation are closely entangled. The markers used show patterns of inter- and intrapopulation diversity that are very atypical compared to the genome as a whole, and reinforce an apparent link between ancestry and racial divergence that is not systematically present otherwise. Despite the legacy of Lewontin's result, therefore, in a major area in which genetics coincides with issues of public interest, methods tend to exaggerate human differences and could thereby contribute to the reification of biological race. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

show abstract

Impact of SNP microarray analysis of compromised DNA on kinship classification success in the context of investigative genetic genealogy

Cited by 41 publications

References 47 publications

Evaluating the Impact of Dropout and Genotyping Error on SNP-Based Kinship Analysis With Forensic Samples

Evaluating the Impact of Dropout and Genotyping Error on SNP-Based Kinship Analysis With Forensic Samples

A machine learning approach for missing persons cases with high genotyping errors

Forensic genetics through the lens of Lewontin: population structure, ancestry and race

Contact Info

Product

Resources

About