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Genin, E.

Publications and source records attributed to Genin, E..

10 recordsLinked to original sources

Context-dependent variant interpretation from Mendelian disease to genetic predisposition: a proof-of-concept using LPL

As genomic sequencing evolves beyond rare disease diagnostics toward population screening and precision medicine, clinical variant interpretation is increasingly challenged by variants whose clinical consequences depend on biological context. Current frameworks, including the ACMG/AMP guidelines, generally assign a single classification to each variant regardless of inheritance state or genetic context, potentially failing to communicate context-dependent clinical consequences. Here, we address this issue using loss-of-function variants in LPL as a uniquely informative model system in which residual physiological LPL activity can be directly quantified in vivo. By systematically integrating published biallelic LPL genotypes, physiological measurements, functional studies, and clinical phenotypes, we identified a biologically meaningful transition at approximately 10% residual physiological LPL activity. Activity below this level was predominantly associated with classical childhood-onset familial chylomicronemia syndrome (FCS), whereas higher activity was associated with phenotypic attenuation and modifier-dependent clinical expression. Furthermore, heterozygous loss-of-function variants exhibited an estimated penetrance of 5-7% for severe hypertriglyceridemia. We therefore propose a context-dependent framework in which biallelic complete- or near-complete loss-of-function genotypes are interpreted as causative for FCS, whereas heterozygous variants are interpreted as predisposing to severe hypertriglyceridemia while retaining recognition of FCS carrier status. Together, our findings demonstrate that clinical variant interpretation should integrate available biological context--including, where relevant, allelic configuration, residual biological function, and penetrance--rather than rely on the intrinsic molecular consequence of the variant alone. More broadly, this framework provides a conceptual model for interpreting variants across the continuum from Mendelian disease to genetic predisposition in the era of precision medicine.

genetics↗

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↗

Enhancing the annotation of small ORF-altering variants using MORFEE: introducing MORFEEdb, a comprehensive catalog of SNVs affecting upstream ORFs in human 5'UTRs

Non-canonical small Open Reading Frames (sORFs) are among main regulators of gene expression. The most studied ones are upstream ORFs (upORFs) located in the 5UTR of coding genes. Internal ORFs (intORFs) in the coding sequence and downstream ORFs (dORFs) in the 3UTR have received less attention. Different bioinformatics tools permit to predict single nucleotide variants (SNVs) altering upORFs, mainly those creating AUGs or deleting stop codons, but no tool predict variants altering non-canonical translation initiation sites and those altering intORFs or dORFs. We propose an upgrade of our MORFEE bioinformatics tool to identify SNVs that may alter all types of sORFs in coding transcripts from a VCF file. Moreover, we generate an exhaustive catalog, named MORFEEdb, reporting all possible SNVs altering existing upORFs or creating new ones in human transcripts and provide an R script for visualizing the results. MORFEEdb has been implemented in the public platform Mobidetails. Finally, the annotation of ClinVar variants with MORFEE reveals that more than 45% of UTR-SNVs can alter upORFs or dORFs. In conclusion, MORFEE and MORFEEdb have the potential to improve the molecular diagnosis of rare human diseases and to facilitate the identification of functional variants from genome-wide association studies of complex traits.

bioinformatics↗

PSAP-genomic-regions: a method leveraging population data to prioritize coding and non-coding variants in whole genome sequencing for rare disease diagnosis

The introduction of next generation sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The PSAP (Population Sampling Probability) method was developed to meet this aim but only for coding variants in exome data. To address the challenge of the analysis of non-coding variants in whole genome sequencing data, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially-generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large datasets of exomes and genomes from the general population. We found that PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than fifty percent of non-coding ClinVar variants, especially those involved in splicing, were among the top 10 variants of the genome. In addition, our approach gave similar results compared to PSAP-genes regarding the scoring of coding variants. On real sequencing data from 6 patients with Cerebral Small Vessel Disease and 9 patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases. PSAP-genomic-regions is implemented as a user-friendly Snakemake workflow, accessible to both researchers and clinicians which can easily integrate up-to-date annotation from large databases. Author summaryIn recent years, improvement in DNA sequencing technologies has allowed the identification of many genes involved in rare diseases. Nonetheless, the molecular diagnosis is still unknown for more than half of rare diseases cases. This is in part due to the large heterogeneity of molecular causes in rare diseases. This also highlights the need for the development of new methods to prioritize pathogenic variants from DNA sequencing data at the scale of the whole genome and not only coding regions. With PSAP-genomic-regions, we offer a strategy to prioritize coding and non-coding variants in whole-genome data from a single individual in need of a diagnosis. The PSAP-genomic-regions combines information on the predicted pathogenicity and frequency of variants in the context of functional regions of the genome. In this work, we compare the PSAP-genomic-regions strategy to other variant prioritization strategies on simulated and real data. We show the better performance of PSAP-genomic-regions over a classical approach based on variant pathogenicity scores alone. PSAP-genomic-regions provides a straightforward approach to prioritize causal pathogenic variants, especially non-coding ones, that are often missed with other strategies and could explain the cause of undiagnosed rare diseases.

genetics↗

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↗

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↗

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the GnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 that is enriched for rare variants in early-onset patients and that was that was missed by standard sliding windows procedures. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages. Author SummaryTechnological progresses have made possible whole genome sequencing at an unprecedented scale, opening up the possibility to explore the role of genetic variants of low frequency in common diseases. The challenge is now methodological and requires the development of novel methods and strategies to analyse sequencing data that are not limited to assessing the role of coding variants. With RAVA-FIRST, we propose a novel strategy to investigate the role of rare variants in the whole-genome that takes benefit from biological information. Especially, RAVA-FIRST relies on testing units that go beyond genes to gather rare variants in the association tests. In this work, we show that this new strategy presents several advantages compared to existing methods. RAVA-FIRST offers an easy and straightforward analysis of genome-wide rare variants, especially the intergenic ones which are frequently left behind, making it a promising tool to get a better understanding of the biology of complex diseases.

genetics↗

Cutevariant: a GUI-based desktop application to explore genetics variations

Cutevariant is a user-friendly GUI based desktop application for genomic research designed to search for variations in DNA samples collected in annotated files and encoded in the Variant Calling Format. The application imports data into a local relational database wherefrom complex filter-queries can be built either from the intuitive GUI or using a Domain Specific Language (DSL). Cutevariant provides more features than any existing applications without compromising on performance. The plugin based architecture provides highly customizable features. Cutevariant is distributed as a multiplatform client-side software under an open source licence and is available at https://github.com/labsquare/Cutevariant. It has been designed from the beginning to be easily adopted by IT-agnostic end-users.

bioinformatics↗

Evaluation of saliva as a source of accurate whole-genome and microbiome sequencing data

This study sets out to establish the suitability of saliva-based whole-genome sequencing (WGS) through a comparison against blood-based WGS. To fully appraise the observed differences, we developed a novel technique of pseudo-replicates. We also investigated the potential of characterising individual salivary microbiomes from non-human DNA fragments found in saliva. We observed the majority of discordant genotype calls between blood and saliva calls fell into known regions of the human genome that are typically sequenced with low confidence; and could be identified by quality control measures. Pseudo-replication demonstrated that the levels of discordance between blood- and saliva-derived WGS data were entirely similar to what one would expect between technical replicates if an individuals blood or saliva had been sequenced twice. Finally, we successfully sequenced salivary microbiomes in parallel to human genomes as demonstrated by a comparison against the Human Microbiome Project. Author SummaryDNA is usually collected from blood for the analysis of human genomes. In France, a new and very large genetic dataset will be created where selected participants will be sent saliva-collection kits in the post as this data collection method presents numerous logistical benefits. It has been previously shown that good quality genetic data can be created from saliva, though existing studies have often not considered the latest technologies or have only analysed a very small number of individuals. In this study, we have analysed genetic data derived from saliva for 39 individuals to give a firm conclusion that the proposed genome sequencing approach of the new French dataset will be capable of provided high quality data by making a comparison to pre-existing genetic data derived from blood for these 39 individuals. In order to do so, we developed a novel method (presented here) to establish the similarity between two sets of genetic data for the same individual that are generated from separate DNA samples. Finally, we have also demonstrated an added bonus of colleting saliva samples: that it is possible to gather both human genetic data and potentially interesting salivary microbiome data at the same time by separating and analysing in parallel human and non-human DNA fragments.

genetics↗

The Genetic History of France

The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and their actual stratification. Although France occupies a particular location at the end of the European peninsula and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2 184 individuals born in France who were enrolled in two independent population cohorts. Using FineStructure, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters match extremely well the geography and overlap with historical and linguistic divisions of France. By modeling the relationship between genetics and geography using EEMS software, we were able to detect gene flow barriers that are similar in the two cohorts and corresponds to major French rivers or mountains. Estimations of effective population sizes using IBDNe program also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to what was observed in other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the fourteenth century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in the genetic studies in Europe that would be useful to medical geneticists but also historians and archeologists.

genetics↗