Biology subjects

Kronenberg, F.

Publications and source records attributed to Kronenberg, F..

7 recordsLinked to original sources

bioRxiv2024

Nanopore sequencing with unique molecular identifiers enables accurate mutation analysis and haplotyping in the complex Lipoprotein(a) KIV-2 VNTR

BackgroundRepetitive genome regions, such as variable number of tandem repeats (VNTR) or short tandem repeats (STR), are major constituents of the uncharted dark genome and evade conventional sequencing approaches. The protein-coding LPA kringle IV type-2 (KIV-2) VNTR (5.6 kb per unit, 1-40 units per allele) is a medically highly relevant example with a particularly intricate structure, multiple haplotypes, intragenic homologies and an intra-VNTR STR. It is the primary regulator of plasma lipoprotein(a) [Lp(a)] concentrations, an important cardiovascular risk factor. However, despite Lp(a) variance is mostly genetically determined, Lp(a) concentrations vary widely between individuals and ancestries. This VNTR region hides multiple causal variants and functional haplotypes. MethodsWe evaluated the performance of amplicon-based nanopore sequencing with unique molecular identifiers (UMI-ONT-Seq) for SNP detection, haplotype mapping, VNTR unit consensus sequence generation and copy number estimation via coverage-corrected haplotypes quantification in the KIV-2 VNTR. We used 15 human samples and low-level mixtures (0.5% to 5%) of KIV-2 plasmids as a validation set. We then applied UMI-ONT-Seq to extract KIV-2 VNTR haplotypes in 48 multi-ancestry 1000-Genome samples and analyzed at scale a poorly characterized STR within the KIV-2 VNTR. ResultsUMI-ONT-Seq detected KIV-2 SNPs down to 1% variant level with high sensitivity, specificity and precision (0.977{+/-}0.018; 1.000{+/-}0.0005; 0.993{+/-}0.02) and accurately retrieved the full-length haplotype of each VNTR unit. Human variant levels were highly correlated with next-generation sequencing (R2=0.983) without bias across the whole variant level range. Six reads per UMI produced sequences of each KIV-2 unit with Q40-quality. The KIV-2 repeat number determined by coverage-corrected unique haplotype counting was in close agreement with droplet digital PCR (ddPCR), with 70% of the samples falling even within the narrow confidence interval of ddPCR. We then analyzed 62,679 intra-KIV-2 STR sequences and identified ancestry-specific STR patterns. Finally, we characterized the KIV-2 haplotype patterns across multiple ancestries. ConclusionsUMI-ONT-Seq accurately retrieves the SNP haplotype and precisely quantifies the VNTR copy number of each repeat unit of the complex KIV-2 VNTR region across multiple ancestries. This study utilizes the KIV-2 VNTR, presenting a novel and potent tool for comprehensive characterization of medically relevant complex genome regions at scale.

genomics↗

bioRxiv2023

Performing highly parallelized and reproducible GWAS analysis on biobank-scale data

MotivationGenome-wide association studies (GWAS) in large biobanks are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60,000 genetic associations across thousands of human diseases and traits have been discovered using a GWAS approach. Due to denser genotyping and increasing sample sizes, researchers are increasingly faced with computational challenges when executing GWAS analysis. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks such as result interpretation and downstream analysis. ResultsHere we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and currently supports single-variant, gene-based and interaction testing. nf-gwas also includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is extensively tested using the unit-style testing framework nf-test to ensure code maintainability, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. Availabilitynf-gwas is free available at https://github.com/genepi/nf-gwas. Contactlukas.forer@i-med.ac.at

bioinformatics↗

bioRxiv2020

Haplocheck: Phylogeny-based Contamination Detection in Mitochondrial and Whole-Genome Sequencing Studies

Within-species contamination is a major issue in sequencing studies, especially for mitochondrial studies. Contamination can be detected by analysing the nuclear genome or by inspecting the heteroplasmic sites in the mitochondrial genome. Existing methods using the nuclear genome are computationally expensive, and no suitable tool for detecting contamination in large-scale mitochondrial datasets is available. Here we present haplocheck, a tool that requires only the mitochondrial genome to detect contamination in both mitochondrial and whole-genome sequencing studies. Haplocheck is able to distinguish between contaminated and real heteroplasmic sites using the mitochondrial phylogeny. By applying haplocheck to the 1000 Genomes Project data, we show (1) high concordance in contamination estimates between mitochondrial and nuclear DNA and (2) quantify the impact of mitochondrial copy numbers on the mitochondrial based contamination results. Haplocheck complements leading nuclear DNA based contamination tools, and can therefore be used as a proxy tool in nuclear genome studies. Haplocheck is available both as a command-line tool at https://github.com/genepi/haplocheck and as a cloud web-service producing interactive reports that facilitates the navigation through the phylogeny of contaminated samples.

bioinformatics↗

bioRxiv2019

Investigation of an LPA KIV-2 nonsense mutation in 11,000 individuals: the importance of linkage disequilibrium structure in LPA genetics.

ObjectiveElevated Lp(a) plasma concentrations are determined mainly genetically by the LPA gene locus, but up to 70% of the coding sequence is located in the so-called "kringle IV type 2" (KIV-2) copy number variation. This region is not resolved by common genotyping technologies and large epidemiological studies on this region are therefore missing. The Arg21Ter variant (R21X, variant frequency {approx}2%) is a functional variant in this region, but it has never been analyzed in large cohorts and is it unknown whether it is captured by genome-wide association studies. Approach and ResultsWe developed a highly sensitive allele-specific qPCR assay and genotyped R21X in 10,910 individuals from three populations (GCKD, KORA F3, KORA F4). R21X carriers showed significantly lower mean Lp(a) concentrations (-11.7 mg/dL [-15.5;-7.82], p=3.39e-32). Of particular note, virtually all R21X carriers also carried the splice mutation rs41272114 (D=0.957, R2=0.275), as confirmed by pulsed-field gel electrophoresis and long-range haplotyping. This proposes that the R21X mutation arose on the background of the rs41272114 splice variant. ConclusionsWe performed the largest epidemiological study on an LPA KIV-2 variant so far. Interestingly, R21X is located on the same haplotype as the splice mutation rs41272114, creating "double-null" LPA alleles that are inactivated by two independent mutations. The effect of the R21X nonsense mutation can thus not be discerned from the effect of rs41272114 splice site mutation. This emphasizes the importance of assessing the complex LD structure within LPA even for functional variants.

genetics↗

bioRxiv2019

A genome-wide analysis of DNA methylation identifies a novel association signal for Lp(a) concentrations in the LPA promoter

Lipoprotein(a) [Lp(a)] is a major cardiovascular risk factor, which is largely genetically determined by one major gene locus, the LPA gene. Many aspects of the transcriptional regulation of LPA are poorly understood and the role of epigenetics has not been addressed yet. Therefore, we conducted an epigenome-wide analysis of DNA methylation on Lp(a) levels in two population-based studies (total n=2208). We identified a CpG site in the LPA promoter which was significantly associated with Lp(a) concentrations. Surprisingly, the identified CpG site was found to overlap the SNP rs76735376. We genotyped this SNP de-novo in three studies (total n=7512). The minor allele of rs76735376 (1.1% minor allele frequency) was associated with increased Lp(a) values (p=1.01e-59) and explained 3.5% of the variation of Lp(a). Statistical mediation analysis showed that the effect on Lp(a) is rather originating from the base change itself and is not mediated by DNA methylation levels. This finding is supported by eQTL data from 153 liver tissue samples from the GTEx project, which shows a significant association of the rs76735376 minor allele with increased LPA expression. In summary, our data supports a functional role of rs76735376 in the regulation of LPA expression.

genetics↗

bioRxiv2019

Cis-epistasis at the LPA locus and risk of coronary artery disease

Identification of epistasis affecting complex human traits has been challenging. Focusing on known coronary artery disease (CAD) risk loci, we explore pairwise statistical interactions between 8,068 SNPs from ten CAD genome-wide association studies (n=30,180). We discovered rs1800769 and rs9458001 in the vicinity of the LPA locus to interact in modulating CAD risk (P=1.75x10-13). Specific genotypes (e.g., rs1800769 CT) displayed either significantly decreased or increased risk for CAD in the context of genotypes of the respective other SNP (e.g., rs9458001 GG vs. AA). In the UK Biobank (n=450,112) significant interaction of this SNP pair was replicated for CAD (P=3.09x10-22), and was also found for aortic valve stenosis (P=6.95x10-7) and peripheral arterial disease (P=2.32x10-4). Identical interaction patterns affected circulating lipoprotein(a) (n=5,953; P=8.7x10-32) and hepatic apolipoprotein(a) (apo(a)) expression (n=522, P=2.6x10-11). We further interrogated potential biological implications of the variants and propose a mechanism explaining epistasis that ultimately may translate to substantial cardiovascular risks.

genetics↗

bioRxiv2018

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[≤]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics↗