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Niels Morling

Publications and source records attributed to Niels Morling.

5 recordsLinked to original sources
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.

stat.AP↗
arXiv2022

Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference

We propose Unity Smoothing (US) for handling inconsistencies between a Bayesian network model and new unseen observations. We show that prediction accuracy, using the junction tree algorithm with US is comparable to that of Laplace smoothing. Moreover, in applications were sparsity of the data structures is utilized, US outperforms Laplace smoothing in terms of memory usage. Furthermore, we detail how to avoid redundant calculations that must otherwise be performed during the message passing scheme in the junction tree algorithm which we refer to as Unity Propagation (UP). Experimental results shows that it is always faster to exploit UP on top of the Lauritzen-Spigelhalter message passing scheme for the junction tree algorithm.

cs.LG↗
arXiv2021

Detecting Outliers in High-dimensional Data with Mixed Variable Types using Conditional Gaussian Regression Models

Outlier detection has gained increasing interest in recent years, due to newly emerging technologies and the huge amount of high-dimensional data that are now available. Outlier detection can help practitioners to identify unwanted noise and/or locate interesting abnormal observations. To address this, we developed a novel method for outlier detection for use in, possibly high-dimensional, datasets with both discrete and continuous variables. We exploit the family of decomposable graphical models in order to model the relationship between the variables and use this to form an exact likelihood ratio test for an observation that is considered an outlier. We show that our method outperforms the state-of-the-art Isolation Forest algorithm on a real data example.

math.ST↗
arXiv2014

The multivariate Dirichlet-multinomial distribution and its application in forensic genetics to adjust for sub-population effects using the θ-correction

In this paper, we discuss the construction of a multivariate generalisation of the Dirichlet-multinomial distribution. An example from forensic genetics in the statistical analysis of DNA mixtures motivates the study of this multivariate extension. In forensic genetics, adjustment of the match probabilities due to remote ancestry in the population is often done using the so-called θ-correction. This correction increases the probability of observing multiple copies of rare alleles and thereby reduces the weight of the evidence for rare genotypes. By numerical examples, we show how the θ-correction incorporated by the use of the multivariate Dirichlet-multinomial distribution affects the weight of evidence. Furthermore, we demonstrate how the θ-correction can be incorporated in a Markov structure needed to make efficient computations in a Bayesian network.

stat.AP↗