Andersen, Mikkel Meyer

Eriksen, Svante

Mogensen, Helle Smidt

Morling, Niels

Nielsen, Malte Bødkergaard

English

    Aalborg UniversitetSNP Genotyping at Low DNA ConcentrationsNielsen, Malte Bødkergaard; Andersen, Mikkel Meyer; Eriksen, Svante; Morling, Niels;Mogensen, Helle SmidtPublication date:2022Document VersionAnden versionLink to publication from Aalborg UniversityCitation for published version (APA):Nielsen, M. B., Andersen, M. M., Eriksen, S., Morling, N., & Mogensen, H. S. (2022). SNP Genotyping at LowDNA Concentrations.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            - Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            - You may not further distribute the material or use it for any profit-making activity or commercial gain            - You may freely distribute the URL identifying the publication in the public portal -Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: November 19, 2022SNP Genotyping at Low DNA ConcentrationsNielsen, M.B.1 (maltebn@math.aau.dk), Andersen, M.M.1,2 (mikl@math.aau.dk), Eriksen, P.S.1 (svante@math.aau.dk),Mogensen, H.S.2 (helle.smidt@sund.ku.dk), Morling, N.1,2 (niels.morling@sund.ku.dk)1 Department of Mathematical Sciences, Faculty of Engineering, Aalborg University, Denmark2 Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences,University of Copenhagen, DenmarkMotivationThe HID_SNP Genotyper v.4.3.1 plug-in (Thermo Fisher Scientific) is efficient atcalling SNP genotypes when the amount ofDNA is sufficiently high. However, for biologicaltraces, the DNA examined may be sparse ordegraded, in which case the number of no-callsincreases to reduce the risk of wrong genotypeclassifications.At the Section of Forensic Genetics at the Uni-versity of Copenhagen (UCPH), we have ob-served wrong genotype classifications from thevariant caller of the HID_SNP Genotyper plug-in with DNA concentrations below 62.5 pg.To overcome this problem for biological traces,UCPH has introduced two additional criteria forSNP calling with the HID_SNP Genotyper plug-in: the coverage must be above 100 reads, andthe heterozygous balance must lie between 2/3and 3/2. If these criteria aren’t met, UCPH de-clares a no-call, leading to quite a lot of no-calls.Purpose and ApplicationsA probabilistic approach to SNP genotyping (orbiallelic genotyping) can reduce the number ofwrong genotype classifications and no-calls.Genotype probabilities from a probabilisticmodel are direct measures of the reliability ofthe called SNPs. By introducing a probabil-ity threshold, q, we will declare a no-call if theprobability for the genotype with the highest es-timated probability is lower than q.Genotype probabilities can be incorporated intoanalysis software, such as Genogeographer, toaccount for the probability of incorrect calls.Our model can be used for biallelic data if thepossible allele signals are interchangeable.Material and MethodsWe made four dilution series of DNA from fiveindividuals in concentrations of 500 pg, 250 pg,125 pg, 62.5 pg, 31.8 pg, and 16 pg DNA. Thedata included 165 autosomal biallelic mark-ers (SNPs) and was arranged in two columnsof reads - one for each of the possible alleles.If both signals from an observation (SNP geno-type) were zero (i.e., zero reads), the observa-tion was removed before analysis.If s1 and s2 are the two possible signals, weapplied a variance stabilizing transformation,f (x) = log(x + 1), and then used a symmet-ric multinomial logistic regression (SMLR),where the genotype, G, was regressed onthe transformed signals, and the heterozygousgenotype was chosen as the baseline:logP (G = a1a1 | s1, s2)P (G = a1a2 | s1, s2)= β0 + β1f (s1) + β2f (s2)logP (G = a2a2 | s1, s2)P (G = a1a2 | s1, s2)= β0 + β2f (s1) + β1f (s2)The HID_SNP Genotyper plug-in gave identicalgenotype classifications for the optimal concen-trations from all four dilution series, so we as-sumed these to be the true genotypes and usedthem as the response in our regression.Methods’ classifications02468log(s 1+1)0 2 4 6 8HID_SNP0 2 4 6 8log(s2 + 1)UCPH0 2 4 6 8Thresholdq = 0.9MissedNo−callWrongMethodβ0 ≠ 0β0 = 0True genotypea1a1a1a2a2a2SMLRNo-calls and wrong classifications for the HID_SNP Genotyper plug-in, the UCPH criteria, and theSMLR model. The grey band around the separation lines of the SMLR model corresponds to aprobability threshold of q = 0.9. Therefore, signal-pairs (log(s1 + 1), log(s2 + 1)) inside this bandwere declared as no-calls. The model was fitted to the full dilution series. For graphical reasons, weplotted only the low concentrations (62.5 pg, 31.8 pg, and 16 pg DNA), as the wrong classificationswere observed with these DNA amounts.No-calls and wrong calls vs. q0501001502000.5 0.6 0.7 0.8 0.9 1.0qNo−calls05101520250.5 0.6 0.7 0.8 0.9 1.0qWrong classificationsConcentration62.5 pg31.8 pg16 pgNo-calls and wrong classifications for the SMLR model as a function of the probability threshold, q.No-callsMethod 62.5 pg 31.8 pg 16 pgHID_SNP 48 83 157UCPH 536 705 874SMLR, q=0.900 15 25 63SMLR, q=0.965 27 47 107SMLR, q=0.998 89 150 257Total calls 3,298 2,633 1,967ConclusionWe have demonstrated that analysis with sym-metric multinomial logistic regression can re-duce the number of no-calls and wrong classifi-cations of SNP genotypes at low DNA amounts.For 62.5 pg and 31.8 pg DNA, we were ableto reduce the number of no-calls by more than95% compared to the UCPH criteria, while stillmaintaining zero wrong calls.For 16 pg DNA, we were able to reduce thenumber of no-calls by 70% compared to theUCPH criteria, while simultaneously reducingthe number of wrong calls by 100%.Future workWe intend to test the SMLR model on data ob-tained with other kits and on data from crimescene samples with degraded and compro-mised DNA.Wrong callsMethod 62.5 pg 31.8 pg 16 pgHID_SNP 1 2 10UCPH 0 0 5SMLR, q=0.900 0 0 10SMLR, q=0.965 0 0 5SMLR, q=0.998 0 0 0Total calls 3,298 2,633 1,967Online poster and figureshttps://github.com/maltebn/ISFG2022

SNP Genotyping at Low DNA Concentrations

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SNP Genotyping at Low DNA Concentrations

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