Biology subjects

Kayser, M.

Publications and source records attributed to Kayser, M..

12 recordsLinked to original sources

bioRxiv2026

Use of the Elston-Stewart algorithm for the efficient calculation of exact pedigree-based Y-STR match probabilities

The formal assessment of a genetic match between a suspect and some biological trace material is one of the key tasks of forensic genetics, particularly in cases of sexual offence. The analysis of Y-chromosomal short tandem repeats (Y-STRs) has proven especially useful in this context. For a long time, however, calculating the probability of a perfect Y-STR profile match under the defense hypothesis that the suspect was not the trace donor posed a great challenge. This was due to the inherent uncertainty about the population of alternative donors, the so-called suspect population. We recently proposed to resolve this controversy by systematically favoring the suspect and considering his close male relatives as the suspect population. However, since the mathematical framework developed for this purpose was simulation-based, its practical application turned out increasingly difficult with increasing pedigree size. Here, we present an adaptation of the so-called Elston-Stewart algorithm, originally developed for the linkage analysis of human genetic diseases, to allow calculation of exact match probabilities in a time that scales linearly with pedigree size. The adapted algorithm was implemented in a publicly available software tool, and its correctness was verified by the comparison of its output with the correct, analytical results obtained for selected example pedigrees. The new implementation mostly outperforms the simulation-based solution, albeit with the important exception of Y-STRs present in multiple copies. Given the increasingly prominent role of such multicopy markers in forensic genetics, the complementary use of both approaches appears the most sensible strategy for the time being.

genetics↗

bioRxiv2025

A multicenter spatial transcriptomics atlas of human tuberculosis and non-tuberculous mycobacterial disease

Granulomas are the hallmark of mycobacterial (MB) infections, forming structured immune environments that contain bacteria but also drive disease persistence. However, their spatial and functional organization remains unclear. Using spatial RNA sequencing on 38 patient samples, we identified five distinct granuloma niches: a necrotic core, an immune-activated inner niche, an inflammatory and an extracellular matrix (ECM)-remodeling middle niche, an outer structural niche, and a tertiary lymphoid structure niche supporting antigen presentation. Immune activity peaks in the inner niche, transitioning to fibrosis at the periphery. Lymph node granulomas display reduced fibroblast involvement but stronger JAK-STAT activation. Mycobacterium tuberculosis (MTB) granulomas exhibit heightened JAK-STAT and IFN-{gamma} signaling, while non-tuberculous mycobacteria (NTM) granulomas show increased hypoxia signatures. Compared to sarcoidosis, MB granulomas feature a structured adaptive immune response, marked by the clustering of plasma cells. Our findings, accessible via https://lab-li.ciim-hannover.de/mb-granuloma/, define key disease signatures, guiding biomarker discovery and therapeutic targeting in granuloma-related diseases.

bioinformatics↗

bioRxiv2025

Genetic Insights of Image-Based Traits: Analysis Pipeline for AI-based Phenotyping, Combined-GWAS, and Federated Learning with Application to the Human Face

Biological images capture rich morphological information and understanding their genetic basis is crucial for elucidating underlying molecular mechanisms, relevant in biomedical, evolutionary, and forensic research and applications. However, progress in understanding the genetic basis of image-based complex traits is hindered by key limitations in the current methodology, regarding both, the degree of which the large image complexity is captured with the phenotyping methods and how the analysis methods deal with the underlying large genetic complexity. Here, we present a robust, scalable, privacy-preserving computer pipeline for analysing image-based complex traits and unveiling their genetic basis by integrating (i) AI-based phenotyping for large-scale extraction of image-based traits across cohorts in a federated learning framework, without sharing individual images; (ii) Combined-GWAS (C-GWAS) for identifying genetic variants underlying the numerous AI-derived image-based traits; and (iii) Explainable AI (XAI)-based image visualization of the identified genetic association effects. In its first application to the example of the human face, we analysed 3D facial images and genomic data from two European cohorts (N=7,309), extracted 200 AI-derived facial traits, identified 43 significantly face associated genetic loci, including 12 novel ones, and replicated 70% of them in an independent European dataset (N=8,246). XAI-based visualization of the identified genetic effects shows the involvement of many of these loci in different parts of the face. Our study provides a privacy-aware and extensible pipeline for investigating the genetic basis of image-based complex traits implemented in a computationally efficient python package available at GitLab and its first application yielded new insights into the genetic architecture of facial shape variation.

genetics↗

bioRxiv2024

qBiCo: A method to assess global DNA conversion performance in epigenetics via single-copy genes and repetitive elements

Human DNA methylation profiling offers great promises in various biomedical applications, including ageing, cancer and even forensics. So far, most DNA methylation techniques are based on a chemical process called sodium bisulfite conversion, which specifically converts non-methylated cytosines into uracils. However, despite the popularity of this approach, it is known to cause DNA fragmentation and loss affecting standardization, while incomplete conversion may result in potential misinterpretation of methylation-based outcomes. To offer the community a solution, we developed qBiCo - a novel quality-control method to address the quantity and quality of bisulfite-converted DNA. qBiCo is a 5-plex, TaqMan(R) probe-based, quantitative (q)PCR assay that amplifies single- and multi-copy DNA fragments of converted and non-converted nature. It estimates four parameters: converted DNA concentration, fragmentation, global conversion efficiency, and potential PCR inhibition. We optimized qBiCo using synthetic DNA standards and assessed it using standard developmental validation criteria, showcasing that qBiCo is reliable, robust and sensitive down to picogram level. We also evaluated its performance by testing decreasing DNA amounts using several commercial bisulfite conversion kits. Depending on the starting DNA quantity, bisulfite-converted DNA recoveries ranged from 8.5 - 100 %, conversion efficiencies from 78 - 99.9 %, while certain kits highly fragment DNA, demonstrating large variability in their performance. Towards building a prototype tool, we further optimized key functionalities, for example, by replacing the poorest performing single-plex assay and creating a more representative DNA standard. Aiming to scale-up and move towards implementation, we successfully transferred and validated our novel method in six different qPCR platforms from different major manufacturers. Overall, with the present study, we offer researchers in the epigenetic field a novel long-awaited QC tool that for the first time allows them to measure key quality and quantity parameters of the most popular DNA conversion process. The tool also enables standardization to prevent inconsistent data and false outcomes in the future, regardless of the downstream experimental analysis of DNA methylation-based research and applications across different fields of biology and biomedicine.

genetics↗

bioRxiv2024

Adaptive predictor-set linear model: an imputation-free method for linear regression prediction on datasets with missing values

Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this short-coming is usually resolved by complete-case analysis or imputation. Both workarounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse and the reduced QR-decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference dataset, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external datasets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness-of-fit, missing value type and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a persons biological age from epigenetic data with promising clinical applications.

genetics↗

bioRxiv2023

Simultaneous mapping of epigenetic inter-haplotype, inter-cell and inter-individual variation via the discovery of jointly regulated CpGs in pooled sequencing data

In the post-GWAS era, great interest has arisen in the mapping of epigenetic inter-individual variation towards investigating the emergence of phenotype in health and disease. Relevant DNA methylation methodologies - epigenome-wide association studies (EWAS), methylation quantitative trait loci (mQTL) mapping and allele-specific methylation (ASM) analysis - can each map certain sources of epigenetic variation and all depend on matching phenotypic/genotypic data. Here, to avoid these requirements, we developed Binokulars, a novel randomization test that identifies signatures of joint CpG regulation from reads spanning multiple CpGs. We tested and benchmarked our novel approach against EWAS and ASM on pooled whole-genome bisulfite sequencing (WGBS) data from whole blood, sperm and combined. As a result, Binokulars simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing and other (still unknown) epigenetic processes. To verify examples of mQTL and polymorphic imprinting, we developed JRC_sorter, another novel tool that classifies regions based on epigenotype models, which we deployed on non-pooled WGBS data from cord blood. In the future, this approach can be applied on larger pools to simultaneously map and characterise inter-haplotype, inter-cell and inter-individual variation in DNA methylation in a cost-effective fashion, a relevant pursuit towards phenome-mapping in the post-GWAS era.

genomics↗

bioRxiv2022

Assessing human genome-wide variation in the Massim region of Papua New Guinea and implications for the Kula trading tradition

The Massim, a cultural region encompassing the southeastern tip of mainland Papua New Guinea (PNG) and nearby islands, is famous for the Kula trading network, in which different valuables circulate in different directions among the islands. To explore Massim genetic history, we generated genome-wide data from across the region and found variable levels of Papuan-related (indigenous) ancestry, including a novel ancestry associated with Rossel Island, and Austronesian-related ancestry that arrived later. We find genetic evidence for different patterns of contact across PNG, the Massim, and the Bismarck and Solomon Archipelagoes for Austronesian-related vs. Papuan-related ancestry, with more geographic restriction for the latter. Moreover, Kula-practicing groups share more genetic similarity than do other groups, and this likely predates the origin of Kula, suggesting that high between-group contact facilitated the formation of Kula. Our study provides the first comprehensive genome-wide assessment of Massim inhabitants and new insights into the fascinating Kula system.

genomics↗

bioRxiv2021

Impact of SNP microarray analysis of compromised DNA on kinship classification success in the context of investigative genetic genealogy

Single nucleotide polymorphism (SNP) data generated with microarray technologies have been used to solve murder cases via investigative leads obtained from identifying relatives of the unknown perpetrator included in accessible genomic databases, referred to as investigative genetic genealogy (IGG). However, SNP microarrays were developed for relatively high input DNA quantity and quality, while SNP microarray data from compromised DNA typically obtainable from crime scene stains are largely missing. By applying the Illumina Global Screening Array (GSA) to 264 DNA samples with systematically altered quantity and quality, we empirically tested the impact of SNP microarray analysis of deprecated DNA on kinship classification success, as relevant in IGG. Reference data from manufacturer-recommended input DNA quality and quantity were used to estimate genotype accuracy in the compromised DNA samples and for simulating data of different degree relatives. Although stepwise decrease of input DNA amount from 200 nanogram to 6.25 picogram led to decreased SNP call rates and increased genotyping errors, kinship classification success did not decrease down to 250 picogram for siblings and 1st cousins, 1 nanogram for 2nd cousins, while at 25 picogram and below kinship classification success was zero. Stepwise decrease of input DNA quality via increased DNA fragmentation resulted in the decrease of genotyping accuracy as well as kinship classification success, which went down to zero at the average DNA fragment size of 150 base pairs. Combining decreased DNA quantity and quality in mock casework and skeletal samples further highlighted possibilities and limitations. Overall, GSA analysis achieved maximal kinship classification success from 800-200 times lower input DNA quantities than manufacturer-recommended, although DNA quality plays a key role too, while compromised DNA produced false negative kinship classifications rather than false positive ones. Author SummaryInvestigative genetic genealogy (IGG), i.e., identifying unknown perpetrators of crime via genomic database-tracing of their relatives by means of microarray-based single nucleotide polymorphism (SNP) data, is a recently emerging field. However, SNP microarrays were developed for much higher DNA quantity and quality than typically available from crime scenes, while SNP microarray data on quality and quantity compromised DNA are largely missing. As first attempt to investigate how SNP microarray analysis of quantity and quality compromised DNA impacts kinship classification success in the context of IGG, we performed systematic SNP microarray analyses on DNA samples below the manufacturer-recommended quantity and quality as well as on mock casework samples and on skeletal remains. In addition to IGG, our results are also relevant for any SNP microarray analysis of compromised DNA, such as for the DNA prediction of appearance and biogeographic ancestry in forensics and anthropology and for other purposes.

genetics↗

bioRxiv2021

Knocking-out the human face genes TBX15 and PAX1 in mice alters facial and other physical morphology

DNA variants in or closed to the human TBX15 and PAX1 genes have been repeatedly associated with facial morphology in independent genome-wide association studies, while their functional roles in determining facial morphology remains to be understood. We generated Tbx15 knockout (Tbx15-/-) and Pax1 knockout (Pax1-/-) mice by applying the one-step CRISPR/Cas9 method. A total of 75 adult mice were used for subsequent phenotype analysis, including 38 Tbx15 mice (10 homozygous Tbx15-/-, 18 heterozygous Tbx15+/-, 10 wild-type WT) and 37 Pax1 mice (12 homozygous Pax1-/-, 15 heterozygous Pax1+/-, 10 WT mice). Facial and other physical morphological phenotypes were obtained from three-dimensional (3D) images acquired with the HandySCAN BLACK scanner. Compared to WT mice, the Tbx15-/- mutant mice had significantly shorter faces (P=1.08E-8, R2=0.61) and their ears were in a significantly lower position (P=3.54E-8, R2=0.62) manifesting an "ear dropping" characteristic. Besides these face alternations, Tbx15-/- mutant mice displayed significantly lower weight as well as shorter body and limb length. Pax1-/- mutant mice showed significantly longer noses (P=1.14E-5, R2=0.46) relative to WT mice, but otherwise displayed less obvious morphological alterations than Tbx15-/- mutant mice did. Because the Tbx15 and Pax1 effects on facial morphology we revealed here in mice are largely consistent with previously reported TBX15 and PAX1 face associations in humans, we suggest that the functional role these two genes play on determining the face of mice is similar to the functional impact their human homologues have on the face of humans. Author SummarySeveral independent genome-wide association studies (GWASs) on human facial morphology highlighted DNA variants in or closed to TBX15 and PAX1 with genome-wide significant association with human facial phenotypes. However, direct evidence on the functional involvement if these genes in the development and determination of facial morphology has not been established as of yet. In the current study, our in vivo gene editing experiments in mice for two well-replicated human face TBX15 and PAX1 genes provide novel evidence on the functional involvement of these two genes in facial and other physical morphology in mice, at least. Tbx15-/- mice showed a shortened facial length and manifesting an ear dropping characteristic, Pax1-/- mice showed an increased nose length. Our geometric morphometrics analysis further indicate that there are significant facial morphology differences between groups (Tbx15-/-and Tbx15+/-, Tbx15-/- and Tbx15+/+, Tbx15+/- and Tbx15+/+, Pax1-/- and Pax1+/+). We provide the first direct functional evidence that two well-known and replicated human face genes, Tbx15 and Pax1, impact facial and other body morphology in mice. The general agreement between our findings in knock-out mice with those from previous GWASs suggests that the functional evidence we established here in mice may also be relevant in humans.

genetics↗

bioRxiv2020

GenNet framework: interpretable neural networks for phenotype prediction

Deep learning is rarely used in population genomics because of the computational burden and challenges in interpreting neural networks. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biological knowledge from public databases, resulting in neural networks that contain only biological plausible connections. We applied the framework to seventeen phenotypes from a case-control study, a population-based study and the UK Biobank. Interpreting the networks revealed well-replicated genes such as HERC2 and OCA2 for hair and eye color and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework obtained an AUC of 0.74 in the held-out test set and identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

bioinformatics↗

bioRxiv2018

Population Replacement in Early Neolithic Britain

The roles of migration, admixture and acculturation in the European transition to farming have been debated for over 100 years. Genome-wide ancient DNA studies indicate predominantly Anatolian ancestry for continental Neolithic farmers, but also variable admixture with local Mesolithic hunter-gatherers1-9. Neolithic cultures first appear in Britain c. 6000 years ago (kBP), a millennium after they appear in adjacent areas of northwestern continental Europe. However, the pattern and process of the British Neolithic transition remains unclear10-15. We assembled genome-wide data from six Mesolithic and 67 Neolithic individuals found in Britain, dating from 10.5-4.5 kBP, a dataset that includes 22 newly reported individuals and the first genomic data from British Mesolithic hunter-gatherers. Our analyses reveals persistent genetic affinities between Mesolithic British and Western European hunter-gatherers over a period spanning Britains separation from continental Europe. We find overwhelming support for agriculture being introduced by incoming continental farmers, with small and geographically structured levels of additional hunter-gatherer introgression. We find genetic affinity between British and Iberian Neolithic populations indicating that British Neolithic people derived much of their ancestry from Anatolian farmers who originally followed the Mediterranean route of dispersal and likely entered Britain from northwestern mainland Europe.

evolutionary biology↗

bioRxiv2017

Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways

The total number of acquired melanocytic nevi on the skin is strongly correlated with melanoma risk. Here we report a meta-analysis of 11 nevus GWAS from Australia, Netherlands, United Kingdom, and United States, comprising a total of 52,506 phenotyped individuals. We confirm known loci including MTAP, PLA2G6, and IRF4, and detect novel SNPs at a genome-wide level of significance in KITLG, DOCK8, and a broad region of 9q32. In a bivariate analysis combining the nevus results with those from a recent melanoma GWAS meta-analysis (12,874 cases, 23,203 controls), SNPs near GPRC5A, CYP1B1, PPARGC1B, HDAC4, FAM208B and SYNE2 reached global significance, and other loci, including MIR146A and OBFC1, reached a suggestive level of significance. Overall, we conclude that most nevus genes affect melanoma risk (KITLG an exception), while many melanoma risk loci do not alter nevus count. For example, variants in TERC and OBFC1 affect both traits, but other telomere length maintenance genes seem to affect melanoma risk only. Our findings implicate multiple pathways in nevogenesis via genes we can show to be expressed under control of the MITF melanocytic cell lineage regulator.

genetics↗