Biology subjects

de Cid, R.

Publications and source records attributed to de Cid, R..

4 recordsLinked to original sources

bioRxiv2025

PolyGenie: An Interactive Platform for Visualising Polygenic Risk Across Multidimensional Cohorts

SummaryPhenome-wide association studies (PheWAS) using polygenic risk scores (PRS) offer a powerful framework for exploring the shared genetic architecture of complex traits across diverse phenotypic domains. However, no standardized, portable pipeline exists to facilitate their systematic execution and visualization in arbitrary population cohorts. We present PolyGenie, an open-source Nextflow pipeline that takes precomputed PRS and cohort phenotype data as input and performs scalable PheWAS analysis across binary and continuous outcomes. The pipeline produces regression results and percentile-based prevalence estimates, which are stored in a SQLite database and visualized through an interactive Dash web application. To demonstrate its utility and reproducibility, we provide a fully worked example using the GCAT cohort, applying 135 PRS to a broad range of clinical, molecular, and lifestyle phenotypes. PolyGenie is designed to be deployed on any cohort with minimal configuration, enabling standardized cross-trait analyses and interactive exploration of genetic risk. Availability and ImplementationPolyGenie is freely available at https://github.com/gcatbiobank/polygenie-pipeline. The GCAT implementation can be accessed at https://polygenie.igtp.cat, and the corresponding adapted Dash source code is available at https://github.com/gcatbiobank/polygenie-gcat. Contactxfarrer@igtp.cat; rdecid@igtp.cat Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

bioRxiv2021

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression models, we present a catalogue of 35,431,441 variants, including 89,178 SVs ([≥]50bp), 30,325,064 SNVs and 5,017,199 indels, across 785 Illumina high coverage (30X) whole-genomes from the Iberian GCAT Cohort, containing 3.52M SNVs, 606,336 indels and 6,393 SVs in median per individual. The haplotype panel is able to impute up to 14,360,728 SNVs/indels and 23,179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.

genomics↗

bioRxiv2019

Assessment of kinship detection using RNA-seq data

Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Sample labelling mistakes are estimated to affect more than 4% of published samples. Therefore, as a method of data quality control, a way to reconstruct pedigrees from RNA-seq data would be useful for confirming the expected relationships. Currently, reconstruction of pedigrees is based mainly on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity-by-descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.

genomics↗

bioRxiv2018

Multidimensional Scaling and Relatedness Research

Multidimensional scaling is a well-known multivariate technique, that is often used in genetics for studying population substructure. In this paper we show that multidimensional scaling of marker data is of relevance for relatedness research. Relatedness is usually investigated by estimating and plotting identity-by-state and identity-by-descent allele-sharing statistics. We show that outlying individuals in a map obtained by multidimensional scaling of genetic variables do not necessarily stem from a different human population, but can be the consequence of relatedness. We propose a method for classifying pairs of individuals into the standard relationship categories that combines genetic bootstrapping, multidimensional scaling and discriminant analysis. We validate our method with simulation studies. Given the variant filtering procedures, our method classifies relationships up to and including the fourth degree with high accuracy (96-97%), using only identity by state. The usefulness of the method is illustrated with data from the 1,000 genomes and the GCAT projects.

genetics↗