Biology subjects

Hofmeister, R. J.

Publications and source records attributed to Hofmeister, R. J..

6 recordsLinked to original sources

bioRxiv2026

Detecting CYP2C19 deletions from genotyping array signals using neural networks

Since copy number variations (CNVs) in pharmacogenes can cause significant alterations in drug metabolism, their reliable detection is of high importance both for large-scale studies and personalized medicine. Whole-genome sequencing, and specifically long-read sequencing, is the gold standard for CNV detection. Despite increasing availability of these technologies, genotyping arrays are still widely used as cost-effective alternatives in biobank and clinical settings, yet calling CNVs based on array intensity signals is challenging due to low base pair resolution. In this work, we developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals. We compared our method to the most widely used algorithm, PennCNV, and demonstrated better performance reaching 100% accuracy in the test dataset. Furthermore, we predicted probe-by-probe CYP2C19 deletion coordinates for all Estonian Biobank samples using nnCNV and PennCNV, and validated these predictions using an identity-by-descent (IBD) sharing method, which also demonstrated superior nnCNV performance. For the deletion samples with conflicting PennCNV and nnCNV predictions, we performed PCR analysis for validation, which showed 97% precision for nnCNV compared to 23% for PennCNV. Finally, we assessed the gradient-based feature importance maps and showed that nnCNV utilizes signal intensity information not only from deletion probes, but also from probes in flanking regions. Our results demonstrate that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.

bioinformatics↗

bioRxiv2026

Evaluating anonymized genome re-identification using polygenic predictions and its implications for data privacy

Re-identification by phenotypic prediction aims to determine whether a genome belongs to a specific individual by comparing the individuals known traits with those predicted from the genome. This type of tracing attack is widely discussed in the genomic privacy literature, yet previous studies have been criticized for overstating its practical risks. Over the past decade, genome-wide association studies (GWAS) with increasing sample size improved the accuracy of phenotypic prediction, potentially enhancing such attacks. To quantify their real-world threat, we developed a probabilistic framework that estimates the likelihood of a match between an individuals observed traits and polygenic scores (PGS) derived from a genome, while accounting for prediction accuracy and genetic and environmental correlations between the traits. We benchmarked this re-identification method and examined how the prior probability (reflecting the a priori chance that a random genome and set of traits correspond to the same person) affects performance. Finally, we assessed whether sensitive information could be inferred through this attack by attempting to predict multiple sensitive haplotypes, such as APOE-{varepsilon}4 (linked with Alzheimers disease). Our re-identification method outperformed a state-of-the-art tool, and reached a precision above 99% for a recall of 40% when considering a prior of 50%. However, after considering real-world settings, we estimated that realistic priors would not exceed 4 x 10-4%, resulting in a precision lower than 0.13% at the same recall (40%). The inference of sensitive genotypes also proved ineffective, as achieving a precision above 50% for identifying APOE-{varepsilon}4 carriers was only possible at a recall below 20%. To conclude, although re-identification by phenotypic prediction is technically feasible, our findings indicate that its effectiveness in real-world conditions is limited. These results counterpoint to earlier claims of severe genomic privacy risks and offer guidance for policymakers, biobank administrators, and research participants.

genetics↗

bioRxiv2025

Parental haplotypes reconstruction in up to 440,209 individuals reveals recent assortative mating dynamics

Assortative mating (AM), the tendency for individuals to choose partners with similar traits, plays an important role in social stratification and has a wide-spread impact on the genetic architecture of complex human traits. A genetic footprint of this behaviour, genetic assortative mating (GAM), has been documented for many traits. However, most existing approaches to estimate GAM either rely on genotyped couples - rare in large biobanks - or on estimates based on gametic phase disequilibrium (GPD), which reflect cumulative effects over multiple generations and cannot detect short-term events. We introduce a novel, scalable approach that infers genetic data for the parental generation to boost statistical power and improve interpretability when estimating GAM in biobank cohorts. Our method reconstructs the two parental haplotypes of biobank individuals by leveraging state-of-the-art inter-chromosomal phasing based on close relative information. By correlating polygenic scores computed separately on maternally and paternally inherited haplotypes, we infer the extent of GAM. Applied to 245,884 individuals from the UK Biobank and 194,325 individuals from the Estonian Biobank, our haplotype-based estimates showed strong concordance with GAM estimates from genotyped mate pairs across 69 traits and improved performance compared to existing GPD-based methods. We replicated previously identified patterns of GAM for many traits (educational attainment, height, BMI, and alcohol consumption), and revealed new ones (overall health and sedentary lifestyle). Temporal and geographic stratification revealed accelerated assortment in recent generations -- particularly for education and height -- and modest differences between urban and rural contexts. Our method enables scalable, interpretable, and generation-specific estimation of GAM in large biobank cohorts, providing new insights into the dynamic nature of mate choice and its impact on the genetic makeup of populations.

genetics↗

bioRxiv2022

Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank

The UK Biobank performed whole-genome sequencing (WGS) and whole-exome sequencing (WES) across hundreds of thousands of individuals, allowing researchers to study the effects of both common and rare variants. Haplotype phasing distinguishes the two inherited copies of each chromosome into haplotypes and unlocks novel analyses at the haplotype level. In this work, we describe a new phasing method, SHAPEIT5, that accurately and rapidly phases large sequencing datasets and illustrates its key features on the UK Biobank WGS and WES data. First, we show that it phases rare variants with high accuracy. For instance, variants found in 1 sample out of 100,000 in the WES data are phased with accuracy above 95%. Second, we show that it can phase singletons, although with moderate accuracy, thereby making their inclusion in downstream analyses possible. Third, we show that the use of UK Biobank as a reference panel increases the accuracy of genotype imputation, an increase that is more pronounced when phased with SHAPEIT5 compared to other methods. Finally, we screen the phased WES data for loss-of-function (LoF) compound heterozygous (CH) events and identify 549 genes in which both gene copies are found knocked out. This list of genes complements current knowledge of gene essentiality in the human genome. We provide SHAPEIT5 in an open-source format, providing researchers with the means to leverage haplotype information in genetic studies.

genomics↗

bioRxiv2021

Parent-of-origin effects in the UK Biobank

Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focussing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies. Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 significant associations, demonstrating that this type of effects contributes to the genetics of complex traits. Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones at loci often unsuspected of being imprinted and, in some cases, previously thought to harbour additive associations.

genomics↗

bioRxiv2020

The molecular basis, genetic control and pleiotropic effects of local gene co-expression

Nearby genes are often expressed as a group. Yet, the prevalence, molecular mechanisms and genetic control of local gene co-expression are far from being understood. Here, by leveraging gene expression measurements across 49 human tissues and hundreds of individuals, we found that local gene co-expression occurs in 13% to 53% genes per tissue. By integrating various molecular assays (e.g. ChIP-seq and Hi-C), we estimated the ability of several mechanisms, such as enhancer-gene targeting, in distinguishing gene pairs that are co-expressed from those that are not. Notably, we identified 32,636 expression quantitative trait loci (eQTLs) which associate to co-expressed gene pairs and often overlap enhancer regions. Due to affecting several genes, these eQTLs are more often associated with multiple human traits than other eQTLs. Our extensive search for local gene co-expression opens the way to comprehend trait pleiotropy and comorbidity and provide functional interpretation of QTL and GWAS findings.

genomics↗