Daniel Kling

FamLink - A user friendly software for linkage calculations in family genetics

The present number of STR loci adopted in relationship testing is chiefly limited to unlinked markers, in most cases residing on different chromosomes. In order to solve more complex cases of relatedness, e.g. deficient paternities and disputed sibships, the number of core loci can be extended. The inclusion of multiple loci on the same chromosome will, however, increase the risk of possible linkage between markers. We present a new software, FamLink, freely available from http://www.FamLink.se, that can perform statistical calculations based on pedigree structures and account for linkage between pairs of markers. In addition, FamLink can simulate genotype data in order to study the effect of accounting for linkage or not. We demonstrate the importance of taking linkage properly into account using examples and real cases.

FamLinkX – implementation of a general model for likelihood computations for X-chromosomal marker data

The use of genetic markers located on the X chromosome has seen a significant increase in the last years and their utility has been well studied. This paper describes the software FamLinkX, freely available at http://www.famlink.se, implementing a new algorithm for likelihood computations accounting for linkage, linkage disequilibrium and mutations. It is obvious that such software is sought for among forensic users as more and more X-chromosomal markers become available. We provide some simulated examples demonstrating the utility of the implementation as well as its application in forensic casework. Though algebraic derivations are generally unfeasible, the paper outlines some theoretical considerations and provides a discussion on the validation of the software. The focus of this paper is to compare the software to existing methods in a forensic setting, perform a validation study as well as to provide an idea of the discriminatory power for X-chromosomal markers.

DNA microarray as a tool in establishing genetic relatedness—Current status and future prospects

In the past decades, microarray technology has definitely put an edge to the field of genetic research. Our aim was to determine whether single nucleotide polymorphism (SNP) microarrays could be used as a tool in establishing genetic relationships where current molecular genetic methods are not sufficient. We used the Genechip, Affymetrix GenomeWide SNP Array 6.0, which detects more than 900,000 SNP markers dispersed throughout the human genome. The intention was to find a good selection of SNP markers that could be used for statistical evaluation of relatedness in a forensic setting. We conducted pairwise comparisons in the R-package FEST as well as pedigree comparisons in Merlin. Our methods were applied on two separate families, where relationships as distant as 3rd cousins were known. In addition, a question about a possible common ancestry between the two families was tested. Relationships as distant as 2nd cousins could be readily distinguished both from unrelated and other, genetically, closer relationships. This was achieved with a selection of 5774 markers, where each pair of markers was separated by a genetic distance of at least 0.5cM (centiMorgan). When considering 3rd cousins, and more distant relationships, the number of markers needs to be extended, consequently decreasing the genetic distance between the markers. However, inclusion of a too large number of markers presents new challenges and our results imply that the use of too dense sets of markers always yields the highest probability for the genetically closest relationship hypothesis. Simulations confirm that this is most probably caused by the fact that the computational model assumes linkage equilibrium between markers, a problem that will be further evaluated. Our results do however suggest that SNP-data derived from microarrays are well suited for kinship determination provided linkage disequilibrium is properly accounted for.

DNA Commission of the International Society for Forensic Genetics (ISFG): Guidelines on the use of X-STRs in kinship analysis

Forensic genetic laboratories perform an increasing amount of genetic analyses of the X chromosome, in particular to solve complex cases of kinship analysis. For some biological relationships X-chromosomal markers can be more informative than autosomal markers, and there are a large number of markers, methods and databases that have been described for forensic use. Due to their particular mode of inheritance, and their physical location on a single chromosome, some specific considerations are required when estimating the weight of evidence for X-chromosomal marker DNA data. The DNA Commission of the International Society for Forensic Genetics (ISFG) hereby presents guidelines and recommendations for the use of X-chromosomal markers in kinship analysis with a special focus on the biostatistical evaluation. Linkage and linkage disequilibrium (association of alleles) are of special importance for such evaluations and these concepts and the implications for likelihood calculations are described in more detail. Furthermore it is important to use appropriate computer software that accounts for linkage and linkage disequilibrium among loci, as well as for mutations. Even though some software exist, there is still a need for further improvement of dedicated software.

A general model for likelihood computations of genetic marker data accounting for linkage, linkage disequilibrium, and mutations.

Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at www.famlink.se). Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.

Models and implementation for relationship problems with dropout

Allelic dropout in relationship problems may commonly appear in areas such as disaster victim identification and the identification of missing persons. If dropout is not accounted for, the results may be incorrect interpretation of profiles, loss of valuable information and biased results. In this paper, we explore different models for dropout in kinship cases and present an efficient implementation for one of the models. The implementation allows for dropout to be handled simultaneously with phenomena like silent alleles and mutations that may also cause discordances in relationship data, in addition to subpopulation correction. The implemented dropout model is freely available in the new version of the Familias software. The concepts and methods are illustrated on real and simulated data.

Mixtures with relatives and linked markers

Mixture DNA profiles commonly appear in forensic genetics, and a large number of statistical methods and software are available for such cases. However, most of the literature concerns mixtures where the contributors are assumed unrelated and the genetic markers are unlinked. In this paper, we consider mixtures of linked markers and related contributors. If no relationships are involved, linkage can be ignored. While unlinked markers can be treated independently, linkage introduces dependencies. The use of linked markers presents statistical and computational challenges, but may also lead to a considerable increase in power since the number of markers available is much larger if we do not require the markers to be unlinked. In addition, some cases that cannot be solved with an unlimited number of unlinked autosomal markers can be solved with linked markers. We focus on two special cases of linked markers: pairs of linked autosomal markers and X-chromosomal markers. A framework is presented for calculation of likelihood ratios for mixtures with general relationships and with linkage between any number of markers. Finally, we explore the effect of linkage disequilibrium, also called allelic association, on the likelihood ratio.

Relationship Inference with Familias and R

Relationship Inference in Familias and R discusses the use of Familias and R software to understand genetic kinship of two or more DNA samples. This software is commonly used for forensic cases to establish paternity, identify victims or analyze genetic evidence at crime scenes when kinship is involved. The book explores utilizing Familias software and R packages for difficult situations including inbred families, mutations and missing data from degraded DNA. The book additionally addresses identification following mass disasters, familial searching, non-autosomal marker analysis and relationship inference using linked markers. The second part of the book focuses on more statistical issues such as estimation and uncertainty of model parameters. Although written for use with human DNA, the principles can be applied to non-human genetics for animal pedigrees and/or analysis of plants for agriculture purposes. The book contains necessary tools to evaluate any type of forensic case where kinship is an issue.

Using Object Oriented Bayesian Networks to Model Linkage, Linkage Disequilibrium and Mutations between STR Markers

In a number of applications there is a need to determine the most likely pedigree for a group of persons based on genetic markers. Adequate models are needed to reach this goal. The markers used to perform the statistical calculations can be linked and there may also be linkage disequilibrium (LD) in the population. The purpose of this paper is to present a graphical Bayesian Network framework to deal with such data. Potential LD is normally ignored and it is important to verify that the resulting calculations are not biased. Even if linkage does not influence results for regular paternity cases, it may have substantial impact on likelihood ratios involving other, more extended pedigrees. Models for LD influence likelihoods for all pedigrees to some degree and an initial estimate of the impact of ignoring LD and/or linkage is desirable, going beyond mere rules of thumb based on marker distance. Furthermore, we show how one can readily include a mutation model in the Bayesian Network; extending other programs or formulas to include such models may require considerable amounts of work and will in many case not be practical. As an example, we consider the two STR markers vWa and D12S391. We estimate probabilities for population haplotypes to account for LD using a method based on data from trios, while an estimate for the degree of linkage is taken from the literature. The results show that accounting for haplotype frequencies is unnecessary in most cases for this specific pair of markers. When doing calculations on regular paternity cases, the markers can be considered statistically independent. In more complex cases of disputed relatedness, for instance cases involving siblings or so-called deficient cases, or when small differences in the LR matter, independence should not be assumed. (The networks are freely available at http://arken.umb.no/~dakl/BayesianNetworks.)

Parameter estimation and uncertainty

The chapter contains examples of procedures for estimation of parameters in probability models for relationship inference. The main examples are the theta correction parameter, the lambda parameter for haplotype estimation, and the mutation rate parameter. The same general Bayesian framework is applied to all these, and can be extended to other parameters. It is also discussed how the likelihoods of likelihood ratios should be computed by integrating out uncertainty in uncertain parameters. One should avoid talking about uncertainty in likelihood ratios, but should instead talk about how likelihood ratios might change when distributions of uncertain parameters change.