Biology subjects

Andersen, M. M.

Publications and source records attributed to Andersen, M. M..

10 recordsLinked to original sources

bioRxiv2026

Are genetically defined "metapopulations" self-evident in YHRD?

In forensic genetics, the evidential value of a match between the Y-chromosomal short tandem repeat (Y-STR) profiles of a trace and a suspect is typically quantified by the frequency of the profile in a population database, particularly the Y-chromosomal Haplotype Reference Database (YHRD). However, for this approach of obtaining a match probability to be valid, the database population must be representative of all plausible alternative trace donors in a given case. Since appropriately defining such a suspect population can be difficult, YHRD highlights so-called metapopulations that comprise profiles from different, geographically dispersed populations with presumed shared ancestry. We investigated whether such metapopulations are self-evident in the current version of YHRD. To this end, we performed classical cluster analysis using allele dissimilarity as a measure of pairwise distance between Y-STR profiles. Our analyses revealed only a weak genetic structure in YHRD the extent of which was inversely proportional to the respective marker mutation rate. This suggests that YHRD cannot be divided into clearly distinguishable subgroups based solely on the genetic information it contains, at least not into subgroups that would correspond closely to the metapopulations highlighted in the database itself. If profile frequencies in metapopulations are to continue to be equated with match probabilities, then a clearer definition of metapopulations and a better justification of their use in forensics are needed.

genetics↗

bioRxiv2025

Likelihood ratio estimation of partial Y-STR profile matches using discrete Laplace models and marginalisation

The discrete Laplace model can be used to estimate the evidential weight of a match between the Y-chromosome short tandem repeat (Y-STR) DNA profiles of the evidence material and a suspect. When the weight of evidence of a match between partial Y-STR profiles is assessed, a discrete Laplace model, restricted to the observed loci in the partial evidence profile, is constructed. However, fitting such a discrete Laplace model is time-consuming since it requires estimating the parameters of the discrete Laplace model and validating the model. We implemented the marginalisation method for discrete Laplace models to estimate the evidential weight of a match between a partial Y-STR evidence profile and a Y-STR reference profile. Since the marginalisation method is based on discrete Laplace models for complete Y-STR profiles, no additional models need to be constructed. We compared the likelihood ratios obtained with (1) the marginalisation method and (2) by fitting new discrete Laplace models restricted to the observed loci in the partial evidence profile. In most cases, the two methods yielded similar likelihood ratios.

genetics↗

bioRxiv2025

Childhood exploration drives population-level innovation in cultural evolution

The societal effects of childrens learning in cultural evolution have been underexplored. Here, we investigate using agent-based models how a propensity for early exploration in childhood contributes to cultural adaptation and the evolution of long human childhood. Using a complex cultural task, we implemented a two-stage strategy for exploring this space - children explore broadly, more likely to learn new behaviours, while adults exploit behaviours already known, incrementally improving them. We found that populations that followed this two-stage strategy achieved higher payoffs in the long term than populations using the two exploration strategies in a random order. Our models point at a just right length of childhood - neither too long, nor too short - allowing individuals enough time to explore before exploiting what they learned. Social learning increased payoffs when agents could copy individuals of a variety of ages. Payoffs decreased under environmental change, especially for long childhoods, because adults did not have enough time to recover between bouts of change.

animal behavior and cognition↗

bioRxiv2025

Probabilities of finding trace profile donors and their paternal relatives in Y-STR reference databases

Forensic investigative genetic genealogy using Y-chromosome short tandem repeat (Y-STR) DNA profiles can give investigative leads in criminal cases by searching for the Y-STR trace profile or similar but not identical Y-STR profiles in relevant Y-STR databases. We conducted a simulation study with Yfiler Plus and PowerPlex(R) Y23 Y-STR profiles to estimate the probabilities of finding matches and near-matches in Y-STR databases. The success rate of finding the trace profile donors or their close relatives was quantified. We used the malan R software package to simulate the populations based on the Wright-Fisher model with the YHRD Y-STR mutation rates where uncertainties were incorporated in a Bayesian manner, a variance in reproductive success of 0.2, and a constant size for 100 generations followed by a 2% growth for 150 generations. Y-STR databases were generated by randomly drawing Y-STR profiles from a Yfiler Plus and PowerPlex(R) Y23 Y-STR population data set, respectively. In a population of 500,066 individuals, a database size of 0.5% of the population resulted in a Y-STR database match probability of ca. 6% and 10% for Yfiler Plus and PowerPlex(R) Y23, respectively. Increasing the database size to 5% of the population resulted in a Y-STR match probability of ca. 41% and 54% for Yfiler Plus and PowerPlex(R) Y23, respectively. When a Y-STR match was found in the database, the probability of one of the individuals with the matching profiles being related within five meioses to the trace donor was ca. 64% and 56% for Yfiler Plus and PowerPlex(R) Y23, respectively, including the cases where the Y-STR profile originated from the donor. In this case, the closest relative in the database was found among the matching individuals with a probability of ca. 91%.

genetics↗

bioRxiv2024

Enhanced SNP Genotyping with Symmetric Multinomial Logistic Regression

In genotyping, determining Single Nucleotide Polymorphisms (SNPs) is standard practice, but it becomes difficult when analysing small quantities of input DNA, as is often required in forensic applications. Existing SNP genotyping methods, such as the HID SNP Genotyper Plugin (HSG) from Thermo Fisher Scientific, perform well with adequate DNA input levels but often produce erroneously called genotypes when DNA quantities are low. To mitigate these errors, genotype quality can be checked with the HSG. However, enforcing the HSGs quality checks decreases the call rate by introducing more no-calls, and it does not eliminate all wrong calls. This study presents and validates a Symmetric Multinomial Logistic Regression (SMLR) model designed to enhance genotyping accuracy and call rate with small amounts of DNA. Comprehensive bootstrap and cross-validation analyses across a wide range of DNA quantities demonstrate the robustness and efficiency of the SMLR model in maintaining high call rates without compromising accuracy compared to the HSG. For DNA amounts as low as 31.25 pg, the SMLR method reduced the rate of no-calls by 50.0% relative to the HSG while maintaining the same rate of wrong calls, resulting in a call rate of 96.0%. Similarly, SMLR reduced the rate of wrong calls by 55.6% while maintaining the same call rate, achieving an accuracy of 99.775%. The no-call and wrong-call rates were significantly reduced at 62.5-250 pg DNA. The results highlight the SMLR models utility in optimising SNP genotyping at suboptimal DNA concentrations, making it a valuable tool for forensic applications where sample quantity and quality may be decreased. This work reinforces the feasibility of statistical approaches in forensic genotyping and provides a framework for implementing the SMLR method in practical forensic settings. The SMLR model applies for genotyping biallelic data with a signal (e.g. reads, counts, or intensity) for each allele. The model can also improve the allele balance quality check.

genetics↗

bioRxiv2022

Weight of evidence of Y-STR matches computed with the discrete Laplace method: Impact of adding a suspect's profile to a reference database

The discrete Laplace method is recommended by multiple parties (including the International Society of Forensic Genetics, ISFG) to estimate the weight of evidence in criminal cases when a suspects Y-STR profile matches the crime scene Y-STR profile. Unfortunately, modelling the distribution Y-STR profiles in the database is time-consuming and requires expert knowledge. When the suspects Y-STR profile is added to the database, as would be the protocol in many cases, the discrete Laplace model must be recomputed. We found that the likelihood ratios with and without adding the suspects Y-STR profile were almost identical with 1,000 or more Y-STR profiles in the database for Y-STR profiles with 8, 12, and 17 loci. Thus, likelihood ratio calculations can be performed in seconds if a an established discrete Laplace model based on at least 1,000 Y-STR profiles is used. A match in a database with 17 Y-STR loci from at least 1,000 male individuals results in a likelihood ratio above 10,000 in approximately 94% of the cases, and above 100,000 in approximately 82% of the cases. We offer a freely available IT tool for estimating the discrete Laplace model of the STR profiles in a database and the likelihood ratio. HighlightsO_LIThe discrete Laplace method is suitable for estimating the weight of evidence of matches with 17 Y-STRs. C_LIO_LILRs based on the discrete Laplace method are 10-100 times higher (in median) than those based on Brenners{kappa} method. C_LIO_LIA database with 17 STRs from at least 1,000 males gives LRs of above 10,000 in approximately 94% of the cases and above 100,000 in approximately 82% of the cases with the discrete Laplace method. C_LIO_LIThe weight of evidence of a matching Y-STR profile is computed within seconds and easily documented when a precomputed discrete Laplace model is available (an IT tool is provided). C_LIO_LI50% of all Yfiler Plus matches are between male relatives within a genetic distance of five meioses. C_LI

genetics↗

bioRxiv2022

SNP calling for the Illumina Infinium Omni5-4 SNP BeadChip kit using the butterfly method

We introduce the "butterfly method" for SNP calling with the Illumina Infinium Omni5-4 BeadChip kit without the use of Illumina GenomeStudio software. The method is a within-sample method and does not use other samples nor population frequencies to call SNPs. The butterfly method is based on a three-component mixture of normal distributions, in which parameters are easily found using the open-source statistical software R. This makes the method transparent, straight-forward to change parameters according to the users needs, and easy to analyse the data within R after the SNPs have been called. We contribute with two open-source R packages that make SNP calling easy by helping with bookkeeping and by giving easy access to meta-information about the SNPs on the Illumina Infinium Omni5-4 BeadChip Kit (including chromosome, probe type, and SNP bases). We test our method on > 4 mio. SNPs and compare the results with those obtained with the GenTrain method used by Illumina GenomeStudio as well as SNPs obtained by PCR-free whole genome sequencing (WGS). We demonstrate two variants of our method: one where we account for potential probe type bias by estimating a separate model for each probe type (type I and type II) and another that uses a general model such that the models parameter estimates do not depend on the sample that is being analysed. We focused on varying the no-call rate and show how it changed the concordance with that of WGS. This is done by using a threshold on the a posteriori probability of belonging to a SNP cluster and by using the number of beads to adjust the stringency of the no-call mechanism. With the butterfly method, we achieve a SNP call rate of around 99% and a SNP concordance of around 99% with the WGS data. By lowering the a posteriori probability threshold for no-calls, we can get a higher call rate fraction than the GenomeStudio and by using a higher a posteriori probability threshold, we can achieve a higher concordance with the WGS data than the GenomeStudio.

bioinformatics↗

bioRxiv2018

How many individuals share a mitochondrial genome?

Mitochondrial DNA (mtDNA) is useful to assist with identification of the source of a biological sample, or to confirm matrilineal relatedness. Although the autosomal genome is much larger, mtDNA has an advantage for forensic applications of multiple copy number per cell, allowing better recovery of sequence information from degraded samples. In addition, biological samples such as fingernails, old bones, teeth and hair have mtDNA but little or no autosomal DNA. The relatively low mutation rate of the mitochondrial genome (mitogenome) means that there can be large sets of matrilineal-related individuals sharing a common mitogenome. Here we present the mitolina simulation software that we use to describe the distribution of the number of mitogenomes in a population that match a given mitogenome, and investigate its dependence on population size and growth rate, and on a database count of the mitogenome. Further, we report on the distribution of the number of meioses separating pairs of individuals with matching mitogenome. Our results have important implications for assessing the weight of mtDNA profile evidence in forensic science, but mtDNA analysis has many non-human applications, for example in tracking the source of ivory. Our methods and software can also be used for simulations to validate models of population history in human or non-human populations.\n\nAuthor SummaryThe maternally-inherited mitochondrial DNA (mtDNA) represents only a small fraction of the human genome, but mtDNA profiles are important in forensic science, for example when a biological evidence sample is degraded or when maternal relatedness is questioned. For forensic mtDNA analysis, it is important to know how many individuals share a mtDNA profile. We present a simulation model of mtDNA profile evolution, implemented in open-source software, and use it to describe the distribution of the number of individuals with matching mitogenomes, and their matrilineal relatedness. The latter is measured as the number of mother-child pairs in the lineage linking two matching individuals. We also describe how these distributions change when conditioning on a count of the profile in a frequency database.

genetics↗

bioRxiv2018

Y-profile evidence: close paternal relatives and mixtures

We recently introduced a new approach to the evaluation of weight of evidence (WoE) for Y-chromosome profiles. Rather than attempting to calculate match probabilities, which is particularly problematic for modern Y-profiles with high mutation rates, we proposed using simulation to describe the distribution of the number of males in the population with a matching Y-profile, both the unconditional distribution and conditional on a database frequency of the profile. Here we further validate the new approach by showing that our results are robust to assumptions about the allelic ladder and the founder haplotypes, and we extend the approach in two important directions. Firstly, forensic databases are not the only source of background data relevant to the evaluation of Y-profile evidence: in many cases the Y-profiles of one or more relatives of the accused are also available. To date it has been unclear how to use this additional information, but in our simulation-based approach its effect is readily incorporated. We describe this approach and illustrate how the WoE that a man was the source of an observed Y-profile changes when the Y-profiles of some of his male-line relatives are also available. Secondly, we extend our new approach to mixtures of Y-profiles from two or more males. Surprisingly, our simulation-based approach reveals that observing a 2-male mixture that includes an alleged contributors profile is almost as strong evidence as observing a matching single-contributor evidence sample, and even 3-male and 4-male mixtures are only slightly weaker.

genetics↗

bioRxiv2017

How Convincing Is A Matching Y-Chromosome Profile?

The introduction of forensic autosomal DNA profiles was controversial, but the problems were successfully addressed, and DNA profiling has gone on to revolutionise forensic science. Y-chromosome profiles are valuable when there is a mixture of male-source and female-source DNA, and interest centres on the identity of the male source(s) of the DNA. The problem of evaluating evidential weight is even more challenging for Y profiles than for autosomal profiles. Numerous approaches have been proposed, but they fail to deal adequately with the fact that men with matching Y-profiles are re-lated in extended patrilineal clans, many of which may not be represented in available databases. This problem has been exacerbated by recent profiling kits with high mutation rates. Because the relevant population is difficult to define, yet the number of matching relatives is fixed as population size varies, it is typically infeasible to derive population-based match probabilities relevant to a specific crime. We propose a conceptually simple solution, based on a simulation model and software to approximate the distribution of the number of males with a matching Y profile. We show that this distribution is robust to different values for the variance in reproductive success and the population growth rate. We also use importance sampling reweighting to derive the distribution of the number of matching males conditional on a database frequency, finding that this conditioning typically has only a modest impact. We illustrate the use of our approach to quantify the value of Y profile evidence for a court in a way that is both scientifically valid and easily comprehensible by a judge or juror.

genetics↗