84 research outputs found
Statistical aspects of forensic genetics:Models for qualitative and quantitative STR data
This PhD thesis deals with statistical models intended for forensic genetics, which is the part of forensic medicine concerned with analysis of DNA evidence from criminal cases together with calculation of alleged paternity and affinity in family reunification cases. The main focus of the thesis is on crime cases as these differ from the other types of cases since the biological material often is used for person identification contrary to affinity. Common to all cases, however, is that the DNA is used as evidence in order to assess the prob-ability of observing the biological material given different hypotheses. Most countries use com-mercially manufactured DNA kits for typing a person’s DNA profile. Using these kits the DNA profile is constituted by the state of 10-15 DNA loci which has a large variation from person to person in the population. Thus, only a small fraction of the genome is typed, but due to the large variability, it is possible to identify individuals with very high probability. These probabil-ities are used when calculating the weight of evidence, which in some cases corresponds to the likelihood of observing a given suspect’s DNA profile in the population. By assessing the probability of the DNA evidence under competing hypotheses the biologica
Review of the Forensic Applicability of Biostatistical Methods for Inferring Ancestry from Autosomal Genetic Markers
The inference of ancestry has become a part of the services many forensic genetic laboratories provide. Interest in ancestry may be to provide investigative leads or identify the region of origin in cases of unidentified missing persons. There exist many biostatistical methods developed for the study of population structure in the area of population genetics. However, the challenges and questions are slightly different in the context of forensic genetics, where the origin of a specific sample is of interest compared to the understanding of population histories and genealogies. In this paper, the methodologies for modelling population admixture and inferring ancestral populations are reviewed with a focus on their strengths and weaknesses in relation to ancestry inference in the forensic context
Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings
The now widespread use of smart heat meters for buildings connected to district heating networks generates data at an unknown extent and temporal resolution. This data encompasses information that enables new data-driven approaches in the building sector. Real-life data of sufficient size and quality are necessary to facilitate the development of such methods, as subsequent analyses typically require a complete equidistant dataset without missing or erroneous values. Thus, this work presents three years (2018-01-03 till 2020-12-31) of screened, interpolated, and imputed data from 3,021 commercial smart heat meters installed in Danish residential buildings. The screening aimed to detect data from not used meters, resolve issues caused by the data storage process and identify erroneous values. Linear interpolation was used to obtain equidistant data. After the screening, 0.3% of the data were missing, which were imputed using a weighted moving average based on a systematic comparison of nine different imputation methods. The original and processed data are published together with the code for data processing (10.5281/zenodo.6563114)
DNAtools: Tools for Analysing Forensic Genetic DNA Data
Development version of the DNAtools R-packag
sparta: Sparse Tables and their Algebra with a View Towards High Dimensional Graphical Models
A graphical model is a multivariate (potentially very high dimensional)
probabilistic model, which is formed by combining lower dimensional components.
Inference (computation of conditional probabilities) is based on message
passing algorithms that utilize conditional independence structures. In
graphical models for discrete variables with finite state spaces, there is a
fundamental problem in high dimensions: A discrete distribution is represented
by a table of values, and in high dimensions such tables can become
prohibitively large. In inference, such tables must be multiplied which can
lead to even larger tables. The sparta package meets this challenge by
implementing methods that efficiently handles multiplication and
marginalization of sparse tables. The package was written in the R programming
language and is freely available from the Comprehensive R Archive Network
(CRAN). The companion package jti, also on CRAN, was developed to showcase the
potential of sparta in connection to the Junction Tree Algorithm. We show, that
jti is able to handle highly complex graphical models which are otherwise
infeasible due to lack of computer memory, using sparta as a backend for table
operations
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