Biology subjects

Herzig, A. F.

Publications and source records attributed to Herzig, A. F..

6 recordsLinked to original sources

bioRxiv2026

Sequencing depth overcomes extraction bias: repurposing human WGS data for salivary microbiome profiling

Large-scale human genomic projects have generated whole-genome sequencing (WGS) data from hundreds of thousands of individuals, primarily to study host genetic variation. When saliva is the DNA source, the resulting datasets also contain microbial reads that are routinely discarded. Here, we investigate whether these host-centric WGS workflows can yield reliable microbiome profiles, effectively doubling the research value of existing data without additional sampling. We compared non-human reads from 39 deeply sequenced saliva samples from the GAZEL cohort (miG dataset; median [~]43 million reads/sample) with 14 samples processed with microbiome-optimized extraction (ASAL; median [~]4.3 million reads/sample), using two complementary classifiers: meteor, a coverage-based mapper against a curated saliva-specific database, and sylph, a k-mer classifier against the Genome Taxonomy Database (GTDB). Despite the absence of microbial lysis optimization, miG samples showed up to 3-fold higher species richness, [~]10-fold greater sequencing depth, and significantly lower inter-sample variability (PERMANOVA R{superscript 2} = 0.10, p = 0.001; BETADISPER p = 0.0036). Rarefaction to 10 reads eliminated most compositional differences, demonstrating that sequencing depth is the primary driver of community stability. Only [~]2% of detected taxa (12 of 592) showed extraction-related differences. The two classifiers exhibited fundamentally different depth-sensitivity profiles, with sylph retaining systematic detection asymmetries even after depth normalization, highlighting that classifier choice introduces biases that affect cross-study comparisons. These results show that biobank WGS data from saliva can be repurposed for robust, population-scale oral microbiome analyses, enabling simultaneous investigation of host genomic variation and the microbiome from the same archived samples. ImportanceSaliva-based whole-genome sequencing datasets generated across various cohorts to study human genetics contain non-human reads that are routinely discarded, thereby overlooking valuable microbial information. We show that these reads are sufficient to reconstruct robust oral microbiome profiles -- without any additional sampling or laboratory work. This finding unlocks a vast archive of existing genomic data for retrospective microbiome research, enabling population-scale studies of oral microbial diversity, host-microbiome interactions, and disease associations at minimal additional cost. We further demonstrate that the choice of taxonomic classifier introduces systematic, depth-dependent biases that persist even after normalization, a practical consideration for any cross-cohort or multi-platform microbiome study.

genomics↗

bioRxiv2025

A simple demonstration of a privacy-preserving de-centralised genotype imputation workflow

Recently, a number of studies have looked at the problem of privacy and data-sharing restrictions in the context of missing genotype imputation servers. This relates to the most typical imputation pipelines which involve a whole-genome sequenced haplotype reference panel being compared to genotyped study individuals (who have missing data to be imputed). Hence, involving two datasets from separate sources coming together in one informatic environment, where relatively complicated statistical models are applied; specifically, hidden Markov modelling. We give a short review of the current literature in this domain, observing three prevalent strategies: complicated data encryption, technical solutions to secure computation environments, and rearrangements of haplotype data in an effort to provide anonymisation. We embarked on a thought experiment to provide a potential fourth type of solution involving federating the different internal tasks within the statistical methods used for imputation. This idea is relevant considering there is currently motivation for federated analyses platforms in Europe for making combined inference across multiple genomic data resources. This allows for very simple manipulations to protect sensitive individual level data, which enable imputation algorithms to complete on simple plain-text files. We provide here an illustration of how such a federated imputation server could be put in place, along with associated code, including a simple implementation of the Li-Stephens haplotype mosaic model to achieve the imputation of missing genotypes. We name our general framework ANONYMP for anonymised imputation. A demonstration of the concept is given involving simulated data generated with msprime. We show that dividing different parts of the required calculations for statistical imputation between several sites is a valuable new avenue in the field of privacy-preserving imputation server development.

genetics↗

bioRxiv2023

A model for co-occurrent assortative mating and vertical cultural transmission and its impact on measures of genetic associations

Assortative mating for a given phenotype is the phenomenon by which mates select each other based on their phenotypic similarity. Other phenomena can create positive correlation between the parents and the offsprings environment: vertical cultural transmission, or dynastic effects. When these phenomena occur together, they induce a gene-environment correlation at the population scale. It will impact genetic measures of associations such as SNP effect size and SNP-heritability. In this paper, we provide a complete mathematical modelling of both assortative mating and vertical cultural transmission in the classical framework of the polygenic additive model. We establish for the first time the theoretical evolution and equilibrium values of all quantities of interest involved. We then derive its consequences on typical genetic epidemiology analyses; including both population and family-based study designs. We show the consequences on heritability estimation, Genome-Wide Association Studies (GWAS), and the variance explained by polygenic scores. We validate our calculations through simple forward-time simulations.

genetics↗

bioRxiv2023

SURFBAT: a surrogate family-based association test building on large imputation reference panels

Genotype-phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analysing populations with fine structure and/or individuals with fine levels of admixture. This can affect power and precision, and is a particularly relevant consideration when control individuals are recruited using geographic selection criteria. Such is the case in France where we have recently created reference panels of individuals anchored to different geographic regions. To make correct comparisons against case groups, who would likely be gathered from large urban areas, new methods are needed. We present SURFBAT (a SURrogate Family Based Association Test) which performs an approximation of the transmission-disequilibrium test. Our method hinges on the application of genotype imputation algorithms to match similar haplotypes between the case and control groups. This permits us to approximate local ancestry informed posterior probabilities of un-transmitted parental alleles of each case individual. SURFBAT provides an association test that is inherently robust to fine-scale population stratification and opens up the possibility of efficiently using large imputation reference panels as control groups for association testing. The method is suitable when the control panel spans the local ancestry spectrum of the case-group population and each control has similar paternal and maternal ancestries. This is the case for our reference panels where individuals have their four grand-parents born in the same geographic area. In contrast to other methods for association testing that incorporate local-ancestry inference, SURFBAT does not require a set of ancestry groups to be defined, nor for local ancestry to be explicitly estimated. We demonstrate the interest of our tool on simulated datasets created from the 1000 Genomes project and the FranceGenRef project, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

genetics↗

bioRxiv2022

Can imputation in a European country be improved by local reference panels? The example of France

France has a population with extensive internal fine-structure; and while public imputation reference panels contain an abundance of European genomes, there include few French genomes. Intuitively, using a study specific panel (SSP) for France would therefore likely be beneficial. To investigate, we imputed 550 French individuals using either the University of Michigan imputation server with the Haplotype Reference Consortium panel, or in-house using an SSP of 850 whole-genome sequenced French individuals. With approximate geo-localization of both our target and SSP individuals we are able to pinpoint different scenarios where SSP-based imputation would be preferred over server-based imputation or vice-versa. We could also show to a high degree of resolution how the proximity of the reference panel to a target individual determined the accuracy of both haplotype phasing and genotype imputation. Previous comparisons of different strategies have shown the benefits of combining public reference panels with SSPs. Getting the best out of both resources simultaneously is unfortunately impractical. We put forward a pragmatic solution where server-based and SSP-based imputation outcomes can be combined based on comparing posterior genotype probabilities. Such an approach can give a level of imputation accuracy markedly in excess of what could be achieved with either strategy alone.

genetics↗

bioRxiv2022

Genetic population structure across Brittany and the downstream Loire basin provides new insights on the demographic history of Western Europe

European genetic ancestry originates from three main ancestral populations - Western hunter-gatherers, early European farmers and Yamnaya Eurasian herders - whose edges geographically met in present-day France. Despite its central role to our understanding of how the ancestral populations interacted and gave rise to modern population structure, the population history of France has remained largely understudied. Here, we analysed the high-coverage whole-genome sequences and genome-wide genotype profiles of respectively 856 and 3,234 present-day individuals from the northern half of France, and merged them with publicly available present-day and ancient Europe-wide genotype datasets. We also explored, for the first time, the whole-genome sequences of six mediaeval individuals (300-1100 CE) from Western France to gain insights into the genetic impact of what is commonly known as the Migration Period in Europe. We found extensive fine-scale population structure across Brittany and the downstream Loire basin, emphasising the need for investigating local populations to better understand the distribution of rare and putatively deleterious variants across space. Overall, we observed an increased population differentiation between the northern and southern sides of the river Loire, which are characterised by different proportions of steppe vs. Neolithic-related ancestry. Samples from Western Brittany carry the largest levels of steppe ancestry and show high levels of allele sharing with individuals associated with the Bell Beaker complex, levels that are only comparable with those found in populations lying on the northwestern edges of Europe. Together, our results imply that present-day individuals from Western Brittany retain substantial legacy of the genetic changes that occurred in Northwestern Europe following the arrival of the Bell Beaker people c. 2500 BCE. Such genetic legacy may explain the sharing of disease-related alleles with other present-day populations from Western Britain and Ireland.

genetics↗