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Mikkel Meyer Andersen

Publications and source records attributed to Mikkel Meyer Andersen.

7 recordsLinked to original sources
arXiv2026

MitoFREQ: A Novel Approach for Mitogenome Frequency Estimation from Top-level Haplogroups and Single Nucleotide Variants

Lineage marker population frequencies can serve as one way to express evidential value in forensic genetics. However, for high-quality whole mitochondrial DNA genome sequences (mitogenomes), population data remain limited. In this paper, we offer a new method, MitoFREQ, for estimating the population frequencies of mitogenomes. MitoFREQ uses the mitogenome resources HelixMTdb and gnomAD, harbouring information from 195,983 and 56,406 mitogenomes, respectively. Neither HelixMTdb nor gnomAD can be queried directly for individual mitogenome frequencies, but offers single nucleotide variant (SNV) allele frequencies for each of 30 "top-level" haplogroups (TLHG). We propose using the HelixMTdb and gnomAD resources by classifying a given mitogenome within the TLHG scheme and subsequently using the frequency of its rarest SNV within that TLHG weighted by the TLHG frequency. We show that this method is guaranteed to provide a higher population frequency estimate than if a refined haplogroup and its SNV frequencies were used. Further, we show that top-level haplogrouping can be achieved by using only 227 specific positions for 99.9% of the tested mitogenomes, potentially making the method available for low-quality samples. The method was tested on two types of datasets: high-quality forensic reference datasets and a diverse collection of scrutinised mitogenomes from GenBank. This dual evaluation demonstrated that the approach is robust across both curated forensic data and broader population-level sequences. This method produced likelihood ratios in the range of 100-100,000, demonstrating its potential to strengthen the statistical evaluation of forensic mtDNA evidence. We have developed an open-source R package `mitofreq` that implements our method, including a Shiny app where custom TLHG frequencies can be supplied.

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arXiv2025

Shotgun DNA sequencing evidence: sample-specific and unknown genotyping error probabilities

Many forensic genetic trace samples are of too low quality to obtain short tandem repeat (STR) DNA profiles as the nuclear DNA they contain is highly degraded (e.g., telogen hairs). Instead, performing shotgun DNA sequencing of such samples can provide valuable information on, e.g., single nucleotide polymorphism (SNP) markers. As a result, shotgun sequencing is starting to gain more attention in forensic genetics and statistical models to correctly interpret such evidence, including properly accounting for sequencing errors, are needed. One such model is the wgsLR model by Andersen et. al. (2025) that enabled evaluating the evidential strength of a comparison between the genotypes in the trace sample and reference sample assuming a single-source contribution to both samples. This paper extends the wgsLR model to allow for different (asymmetric) genotyping error probabilities (e.g., from a low quality trace sample and a high quality reference sample). The model was also extended to handle unknown genotyping error probabilities via both maximising profile likelihood and using a prior distribution. The sensitivity of the wgsLR model against overdispersion was also investigated and it was found robust against it. It was also found that handling an unknown genotyping error probability of the trace sample with the methods having a sufficient number of independent markers gave concordant weight of evidence (WoE) under both the hypotheses (same or different individuals being donors of trace and reference sample). It was found more conservative to use a too small trace sample genotyping error probability rather than a too high genotyping error probability as the latter can explain genotype inconsistencies by errors rather than due to two different individuals being the donors of the trace sample and reference sample. The extensions of the model are implemented in the R package wgsLR.

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arXiv2024

Shotgun DNA sequencing for human identification: Dynamic SNP selection and likelihood ratio calculations accounting for errors

In forensic genetics, short tandem repeats (STRs) are used for human identification (HID). Degraded biological trace samples with low amounts of short DNA fragments (low-quality DNA samples) pose a challenge for STR typing. Predefined single nucleotide polymorphisms (SNPs) can be amplified on short PCR fragments and used to generate SNP profiles from low-quality DNA samples. However, the stochastic results from low-quality DNA samples may result in frequent locus drop-outs and insufficient numbers of SNP genotypes for convincing identification of individuals. Shotgun DNA sequencing potentially analyses all DNA fragments in a sample in contrast to the targeted PCR-based sequencing methods and may be applied to DNA samples of very low quality, like heavily compromised crime-scene samples and ancient DNA samples. Here, we developed a statistical model for shotgun sequencing, sequence alignment, and genotype calling. Results from replicated shotgun sequencing of buccal swab (high-quality samples) and hair samples (low-quality samples) were arranged in a genotype-call confusion matrix to estimate the calling error probability by maximum likelihood and Bayesian inference. We developed formulas for calculating the evidential weight as a likelihood ratio (LR) based on data from dynamically selected SNPs from shotgun DNA sequencing. The method accounts for potential genotyping errors. Different genotype quality filters may be applied to account for genotyping errors. An error probability of zero resulted in the forensically commonly used LR formula. When considering a single SNP marker's contribution to the LR, error probabilities larger than zero reduced the LR contribution of matching genotypes and increased the LR in the case of a mismatch. We developed an open-source R package, wgsLR, which implements the method, including estimating the calling error probability and calculating LR values.

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arXiv2023

Computer Algebra in R Bridges a Gap Between Mathematics and Data in the Teaching of Statistics and Data Science

The capability of R to do symbolic mathematics is enhanced by the caracas package. This package uses the Python computer algebra library SymPy as a back-end but caracas is tightly integrated in the R environment. This enables the R user with symbolic mathematics within R at a high abstraction level rather than using text strings and text string manipulation as the case would be if using SymPy from R directly. We demonstrate how mathematics and statistics can benefit from bridging computer algebra and data via R. This is done thought a number of examples and we propose some topics for small student projects. The caracas package integrates well with e.g. Rmarkdown, and as such creation of scientific reports and teaching is supported.

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arXiv2021

Computer Algebra in R with caracas

The capability of R to do symbolic mathematics is enhanced by the caracas package. This package uses the Python computer algebra library SymPy as a back-end but caracas is tightly integrated in the R environment, thereby enabling the R user with symbolic mathematics within R. Key components of the caracas package are illustrated in this paper. Examples are taken from statistics and mathematics. The caracas package integrates well with e.g. Rmarkdown, and as such creation of scientific reports and teaching is supported.

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arXiv2012

Efficient Forward Simulation of Fisher-Wright Populations with Stochastic Population Size and Neutral Single Step Mutations in Haplotypes

In both population genetics and forensic genetics it is important to know how haplotypes are distributed in a population. Simulation of population dynamics helps facilitating research on the distribution of haplotypes. In forensic genetics, the haplotypes can for example consist of lineage markers such as short tandem repeat loci on the Y chromosome (Y-STR). A dominating model for describing population dynamics is the simple, yet powerful, Fisher-Wright model. We describe an efficient algorithm for exact forward simulation of exact Fisher-Wright populations (and not approximative such as the coalescent model). The efficiency comes from convenient data structures by changing the traditional view from individuals to haplotypes. The algorithm is implemented in the open-source R package 'fwsim' and is able to simulate very large populations. We focus on a haploid model and assume stochastic population size with flexible growth specification, no selection, a neutral single step mutation process, and self-reproducing individuals. These assumptions make the algorithm ideal for studying lineage markers such as Y-STR.

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