Biology subjects

Cisca Wijmenga

Publications and source records attributed to Cisca Wijmenga.

4 recordsLinked to original sources

bioRxiv2016

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Here, we present GAVIN, a new method that delivers accurate classification of variants for next-generation sequencing molecular diagnostics. It is based on gene-specific calibrations of allele frequencies (from the ExAC database), effect impact (using SnpEff) and estimated deleteriousness (CADD scores) for >3,000 genes. In a benchmark on 18 clinical gene sets, we achieved a sensitivity of 91.6%, with a specificity of 78.2%. This accuracy was unmatched by 12 other tools we tested. We provide GAVIN as an online MOLGENIS service to annotate VCF files, and as open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin.

Bioinformatics↗

bioRxiv2016

A high-quality reference panel reveals the complexity and distribution of structural genome changes in a human population

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation.\n\nHere, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals. Our findings are essential for genome-wide association studies.

Genetics↗

bioRxiv2014

An introduction to LifeLines DEEP: study design and baseline characteristics

There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects and thereby contributing to systems epidemiology. This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. For this unique sub-cohort, LifeLines DEEP, additional blood (n=1387), exhaled air (n=1425), fecal samples (n=1248) and gastrointestinal health questionnaires (n=1176) were collected for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation on a wealth of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP.

Genomics↗

bioRxiv2014

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

Given increasing numbers of RNA-seq samples in the public domain, we studied to what extent expression quantitative trait loci (eQTLs) and allele-specific expression (ASE) can be identified in public RNA-seq data while also deriving the genotypes from the RNA-seq reads. 4,978 human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though this data originated from many different laboratories, samples reflecting the same cell-type clustered together, suggesting that technical biases due to different sequencing protocols were limited. We derived genotypes from the RNA-seq reads and imputed non-coding variants. In a joint analysis on 1,262 samples combined, we identified cis-eQTLs effects for 8,034 unique genes. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become relevant for studying tissue-specific effects of rare pathogenic genetic variants.

Genetics↗