Biology subjects

Santoni, F. A.

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

6 recordsLinked to original sources

bioRxiv2021

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

CoverageMaster (CoM) is a Copy Number Variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome (WES) and genome (WGS) data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model (HMM). CoM processes WES and WGS data at nucleotide scale resolution and accurately detect and visualize full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such array CGH and MLPA in the near future.

bioinformatics↗

bioRxiv2020

Dominant monoallelic variant in the PAK2 gene causes Knobloch syndrome type 2

Knobloch syndrome is an autosomal recessive phenotype mainly characterized by retinal detachment and encephalocele caused by biallelic pathogenic variants in the COL18A1 gene. However, there are patients clinically diagnosed as Knobloch syndrome with unknown molecular etiology not linked to COL18A1. We studied an historical pedigree (published in 1998) designated as KNO2 (Knobloch type 2 syndrome with intellectual disability, autistic behavior, retinal degeneration, encephalocele). Whole exome sequencing of the two affected siblings and the normal parents resulted in the identification of a PAK2 non-synonymous substitution p.(Glu435Lys) as a causative variant. The variant was monoallelic and apparently de novo in both siblings indicating a likely germline mosaicism in one of the parents; the mosaicism however could not be observed after deep sequencing of blood parental DNA. PAK2 encodes a member of a small group of serine/threonine kinases; these P21-activating kinases (PAKs) are essential in signal transduction and cellular regulation (cytoskeletal dynamics, cell motility, death and survival signaling, and cell cycle progression). Structural analysis of the PAK2 p.(Glu435Lys) variant which is located in the kinase domain of the protein predicts a possible compromise in the kinase activity. Functional analysis of the p.(Glu435Lys) PAK2 variant in transfected HEK293T cells results in a partial loss of the kinase activity. PAK2 has been previously suggested as an autism related gene. Our results show that PAK2 induced phenotypic spectrum is broad and not fully understood. We conclude that the KNO2 syndrome in the studied family is dominant and caused by a deleterious variant in the PAK2 gene.

genetics↗

bioRxiv2020

The role of gene expression on human sexual dimorphism: too early to call

The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. To explore this scenario, we performed a genome-wide analysis of sex-specific whole blood RNA-seq eQTLs from 3,447 individuals. Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-specific cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-specific eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-specific trait-associations. Power analyses using real eQTL- and causal effect sizes showed that millions of samples would be necessary to observe sex-specific trait associations that are fully driven by sex-specific cis-eQTLs. Compensatory effects may further hamper their detection. In line with this observation, we confirmed that the sex-specific trait-associations detected so far are not driven by sex-specific cis-eQTLs.

genetics↗

bioRxiv2018

Time and space dimensions of gene dosage imbalance of aneuploidies revealed by single cell transcriptomes.

The mechanisms underlying cellular and organismal phenotypes due to copy number alterations (CNA) are not fully understood. Aneuploidy is a major source of gene dosage imbalance due to CNA and viable human trisomies are model disorders of altered gene expression. To understand the cellular impact of gene dosage imbalance, we studied gene and allele specific expression (ASE) of 9668 single-cell fibroblasts in trisomies T21, T18, T13 and T8. To limit the bias of interindividual noise, all comparisons between euploid and trisomic single-cells were performed on an isogenic setting for all trisomies studied. Initially we examined 928 single cells with deep RNA-Seq. For T21 we used fibroblasts from one pair of monozygotic twins discordant for T21 and from mosaic T21. For T18, T13 and T8 we analyzed single cells from mosaic individuals. Single-cell analyses revealed inconsistencies concerning the overexpression of some genes observed in differential trisomic vs euploid bulk RNAseq while this imbalance was not detectable in trisomic vs. euploid single cells. Moreover, ASE profiling of all single cells uncovered a substantial monoallelic pattern of expression in the trisomic fraction of the genome. By classifying genes according to the level of mono and bi-allelic transcription, we have observed that, for genes with monoallelic and low-to-average expression, the altered gene dosage is mainly due to the higher fraction of cells simultaneously expressing these genes in the trisomic samples. These results were confirmed in a further experiment of 8740 single fibroblasts from the monozygotic twins discordant for T21 samples. We conclude that gene dosage imbalance is of bidimensional nature: over time (simultaneous expression of all alleles resulting in increased accumulation of RNA of copy altered genes in each single cell) as previously stated, and over space (increased fraction of cells simultaneously expressing copy altered genes). These results strongly suggest that each class of genes contributes to the phenotypic variability of trisomies according to its temporal and spatial behavior and propose an improved model to understand the effects of copy number alterations.

genomics↗

bioRxiv2018

Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Genome-wide association studies (GWAS) identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs (eQTLs), indicating their potential involvement in the regulation of gene expression.\n\nHere, we propose an advanced transcriptome-wide summary statistics-based Mendelian Randomization approach (called TWMR) that uses multiple SNPs jointly as instruments and multiple gene expression traits as exposures, simultaneously.\n\nWhen applied to 43 human phenotypes it uncovered 2,369 genes whose blood expression is putatively associated with at least one phenotype resulting in 3,913 gene-trait associations; of note, 36% of them had no genome-wide significant SNP nearby in previous GWAS analysis. Using independent association summary statistics (UKBiobank), we confirmed that the majority of these loci were missed by conventional GWAS due to power issues. Noteworthy among these novel links is educational attainment-associated BSCL2, known to carry mutations leading to a mendelian form of encephalopathy. We similarly unraveled novel pleiotropic causal effects suggestive of mechanistic connections, e.g. the shared genetic effects of GSDMB in rheumatoid arthritis, ulcerative colitis and Crohns disease.\n\nOur advanced Mendelian Randomization unlocks hidden value from published GWAS through higher power in detecting associations. It better accounts for pleiotropy and unravels new biological mechanisms underlying complex and clinical traits.

genetics↗

bioRxiv2018

Extensive cellular heterogeneity of X inactivation revealed by single-cell allele-specific expression in human fibroblasts.

In eutherian mammals, X chromosome inactivation (XCI) provides a dosage compensation mechanism where in each female cell one of the two X chromosomes is randomly silenced. However, some genes on the inactive X chromosome and outside the pseudoautosomal regions escape from XCI and are expressed from both alleles (escapees). Given the relevance of the escapees in biology and medicine, we investigated XCI at an unprecedented single-cell resolution. We combined deep single-cell RNA sequencing with whole genome sequencing to examine allelic specific expression (ASE) in 935 primary fibroblast and 48 lymphoblastoid single cells from five female individuals. In this framework we integrated an original method to identify and exclude doublets of cells. We have identified 55 genes as escapees including 5 novel escapee genes. Moreover, we observed that all genes exhibit a variable propensity to escape XCI in each cell and cell type, and that each cell displays a distinct expression profile of the escapee genes. We devised a novel metric, the Inactivation Score (IS), defined as the mean of the allelic expression profiles of the escapees per cell, and discovered a heterogeneous and continuous degree of cellular XCI with extremes represented by \"inactive\" cells, i.e., exclusively expressing the escaping genes from the active X chromosome, and \"escaping\" cells, expressing the escapees from both alleles. Intriguingly we found that XIST is the major genetic determinant of IS, and that XIST expression, higher in G0 phase, is negatively correlated with the expression of escapees, inactivated and pseudoautosomal genes. In this study we use single-cell allele specific expression to identify novel escapees in different tissues and provide evidence of an unexpected cellular heterogeneity of XCI driven by a possible regulatory activity of XIST.

genetics↗