Emmanuelle Génin

Understanding the fine-scale genetic structure of human populations is essential for both demographical history and genomic medicine. The POPGEN project was initiated as part of the France Genomic Medicine Initiative to construct a whole-genome reference panel representative of the ancestral genetic diversity that existed across metropolitan France at the beginning of the 20th century. Building on previous studies that reported significant fine-scale population structure within France, POPGEN aimed to improve geographic coverage and generate regional allele frequency data to support medical and population genetics applications. We recruited 15,000 participants from the largest French population-based cohort, CONSTANCES, based on the birthplace proximity of their four grandparents and sent saliva kits at their homes. The high return rate (68.3%) demonstrates feasibility of large-scale self-sampling in the general population. Genotyping data from the 9,772 samples that could be processed confirm the presence of widespread fine-scale structure across France, and extend this finding to previously underrepresented regions. These results highlight the value of recruitment strategies based on grandparents’ origin for capturing subtle genetic structure. The development of sampling strategies that best capture genetic diversity at the finest scales will indeed be essential to characterize population structure across Europe and to enable more accurate analyses in medical genetics, especially when addressing rare variant interpretation and fine-scale ancestry adjustment for association tests and polygenic scores calibration. The thorough description of our recruitment strategy would provide useful insights for other initiatives like the Genome of Europe (GoE) project, which aims to generate whole-genome references encompassing European diversity.

Public databases such as gnomAD inform on genetic variants observed in people originating from Europe. However, regional ancestries are not equally represented. In particular, individuals of French origin remain under-represented in international sequencing datasets. To address this gap in the European geo-genetic map and contribute to European initiatives in Genomic Medicine, we sequenced the whole genomes of 856 individuals selected based on the birthplaces of their four grandparents to build a first French-ancestry reference panel. We report here a comprehensive catalog of genetic variants, their geographical distribution across metropolitan France, and their overlap with variants referenced in gnomAD. These data start to fill the French gap in the available European data. They will support international efforts to catalog genomic variation among regional populations and to develop reference genome panels for case-control studies and genotype imputation, a prerequisite for improving our understanding of how genomic variability influences health and disease.

Genetic polymorphisms are common in pharmacogenes, with sometimes important implications for drug metabolism. Assessing the correct enzyme phenotype from genetic data is thus a crucial step into the development of personalized medicine. Many bioinformatics star‐allele callers have been developed for this purpose of identifying the correct star alleles and the associated phenotype, each of them having their specific method and limitations. Despite the important benchmarks that have been made so far, their performances have not yet been fully explored depending on various parameters, such as the type of genetic data provided as input or the individuals' ancestry. Hence, we provide a multi‐gene, multi data‐type comparison of the accuracy of four commonly used and open‐access star‐allele callers: PyPGx, ursaPGx, PharmCAT, and Aldy. We found that PyPGx and Aldy are overall more performant than the others, except for CYP2D6 where ursaPGx was the most accurate with its CYP2D6 dedicated caller that relies on the Cyrius software. Comparing to the commercial solution DRAGEN, PyPGx, and Aldy showed better results, except for CYP2D6 where DRAGEN performed best. When only SNP‐chip or low‐pass sequencing data is available, the use of imputation greatly improves the performance of star‐allele callers, allowing performance comparable to that achieved with sequencing data. We also analyzed how concordance between star‐allele callers varies depending on population ancestry. Our findings offer guidance on the choice of star‐allele caller, depending on the pharmacogene being studied and the resolution of available genetic data.

Genetic polymorphisms are common in pharmacogenes, with sometimes important implications for drug metabolism. Assessing the correct enzyme phenotype from genetic data is thus a crucial step into the development of personalized medicine. Many bioinformatics star-allele callers have been developed for this purpose of identifying the correct star alleles and the associated phenotype, each of them having their specific method and limitations. Despite the important benchmarks that have been made so far, their performances have not been yet fully explored depending on various parameters, such as the type of genetic data provided as input, or the individuals’ ancestry. Hence, we provide a multi-gene, multi data-type comparison of the accuracy of four commonly used and open-access star-allele callers: PyPGx, ursaPGx, PharmCAT and Aldy. We found that PyPGx and Aldy are overall more performant than the others. Moreover, using imputation or low-pass sequencing data can enhance the accuracy of star-allele callers compared to SNP-chip genotyping data only. Finally, we noticed that the concordance between star-allele callers is highly dependent on population ancestry. Our study provides new recommendations about the algorithm clinicians and researchers should use regarding the pharmacogene and the type of data they have access to.

Digenic inheritance is characterized by the combined alteration of two different genes leading to a disease. It could explain the etiology of many currently undiagnosed rare diseases. With the advent of next-generation sequencing technologies, the identification of digenic inheritance patterns has become more technically feasible, yet still poses significant challenges without any gold standard method. Here, we present a comprehensive overview of the existing methods developed to detect digenic inheritance in sequencing data and provide a classification in cohort-based and individual-based methods. The latter category of methods appeared the most applicable to rare diseases, especially the ones not needing patient phenotypic description as input. We discuss the availability of the different methods, their output and scalability to inform potential users. Focusing on methods to detect digenic inheritance in the case of very rare or heterogeneous diseases, we propose a benchmark using different real-life scenarios involving known digenic and putative neutral pairs of genes. Among these different methods, DiGePred stood out as the one giving the least number of false positives, ARBOCK as giving the greatest number of true positives, and DIEP as having the best balance between both. By synthesizing the state-of-the-art techniques and providing insights into their practical utility, this benchmark serves as a valuable resource for researchers and clinicians in selecting suitable methodologies for detecting digenic inheritance in a wide range of disorders using sequencing data.

Non-canonical small open reading frames (sORFs) are among the main regulators of gene expression. The most studied of these are upstream ORFs (upORFs) located in the 5′-untranslated region (UTR) of coding genes. Internal ORFs (intORFs) in the coding sequence and downstream ORFs (dORFs) in the 3′UTR have received less attention. Different bioinformatics tools permit the prediction of single nucleotide variants (SNVs) altering upORFs, mainly those creating AUGs or deleting stop codons, but no tool predicts 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 > 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.

Introduction: Next-generation sequencing (NGS) data analysis has become an integral part of clinical genetic diagnosis, raising the question of variant prioritization. The Population Sampling Probability (PSAP) method has been developed to tackle the issue of variant prioritization in the exome of a single patient, by leveraging allele frequencies from population databases and a variant pathogenicity score. Methods: Here, we present Easy-PSAP, a completely new implementation of the PSAP method comprising two user-friendly and highly adaptable pipelines. Easy-PSAP allows the gene-based recalibration of any in silico pathogenicity prediction score compared to scores of variants seen in the general population, including popular scores like CADD or AlphaMissense. Easy-PSAP can evaluate genetic variants at the scale of a whole exome or a whole genome using information from the latest population and annotation databases. Results: Through simulations on synthetic disease exomes, we show that Easy-PSAP is able to rank more than 50% of causal pathogenic variants in the top 10 variants for an autosomal dominant model of transmission and in top 1 for an autosomal recessive model of transmission. Discussion: These findings, along with the accessibility of the pipeline to both researchers and clinicians, make Easy-PSAP a state-of-the-art tool for variant prioritization in NGS data that can continue to evolve as new frameworks and databases become available. Easy-PSAP is implemented in R and bash within an open-source Snakemake framework. It is available on GitHub alongside conda environments containing the required dependencies (https://github.com/msogloblinsky/Easy-PSAP).

Genotype–phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analyzing 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. This is achieved by assuming haplotypes from the imputation panel are well-matched for ancestry with the case individuals. When the first haplotype of an individual from the imputation panel matches that of a case individual, it is assumed that the second haplotype of the same reference individual can be used as a locally ancestry matched control haplotype and to approximately impute un-transmitted parental alleles. 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. 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, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

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 Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, 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 data sets of exomes and genomes from the general population. PSAP‐genomic‐regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP‐genomic‐regions, more than 50% of non‐coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine 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.

The demographical history of France remains largely understudied despite its central role toward understanding modern population structure across Western Europe. Here, by exploring publicly available Europe-wide genotype datasets together with the genomes of 3234 present-day and six newly sequenced medieval individuals from Northern France, we found extensive fine-scale population structure across Brittany and the downstream Loire basin and increased population differentiation between the northern and southern sides of the river Loire, associated with higher proportions of steppe vs. Neolithic-related ancestry. We also found increased allele sharing between individuals from Western Brittany and those associated with the Bell Beaker complex. Our results emphasise the need for investigating local populations to better understand the distribution of rare (putatively deleterious) variants across space and the importance of common genetic legacy in understanding the sharing of disease-related alleles between Brittany and people from western Britain and Ireland.