xQTL workbench: a scalable web environment for multi-level QTL analysis

Tuberculosis severity associates with variants and eQTLs related to vascular biology and infection-induced inflammation

Background

Tuberculosis (TB) remains a major public health problem globally, even compared to COVID-19. Genome-wide studies have failed to discover genes that explain a large proportion of genetic risk for adult pulmonary TB, and even fewer have examined genetic factors underlying TB severity, an intermediate trait impacting disease experience, quality of life, and risk of mortality. No prior severity analyses used a genome-wide approach.

Methods and findings

As part of our ongoing household contact study in Kampala, Uganda, we conducted a genome-wide association study (GWAS) of TB severity measured by TBScore, in two independent cohorts of culture-confirmed adult TB cases (n = 149 and n = 179). We identified 3 SNPs (P<1.0 x 10–7) including one on chromosome 5, rs1848553, that was GWAS significant (meta-analysis p = 2.97x10-8). All three SNPs are in introns of RGS7BP and have effect sizes corresponding to clinically meaningful reductions in disease severity. RGS7BP is highly expressed in blood vessels and plays a role in infectious disease pathogenesis. Other genes with suggestive associations defined gene sets involved in platelet homeostasis and transport of organic anions. To explore functional implications of the TB severity-associated variants, we conducted eQTL analyses using expression data from Mtb-stimulated monocyte-derived macrophages. A single variant (rs2976562) associated with monocyte SLA expression (p = 0.03) and subsequent analyses indicated that SLA downregulation following MTB stimulation associated with increased TB severity. Src Like Adaptor (SLAP-1), encoded by SLA, is highly expressed in immune cells and negatively regulates T cell receptor signaling, providing a potential mechanistic link to TB severity.

Conclusions

These analyses reveal new insights into the genetics of TB severity with regulation of platelet homeostasis and vascular biology being central to consequences for active TB patients. This analysis also reveals genes that regulate inflammation can lead to differences in severity. Our findings provide an important step in improving TB patient outcomes.

Similarities between plant traits based on their connection to underlying gene functions

Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between 'leaf senescence' and 'aspartic acid', as well as between 'days to maturity' and 'choline'. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes.

reGenotyper: Detecting mislabeled samples in genetic data

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis.

Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background

Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers.

Results

The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.

We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population.

Conclusion

The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0475-6) contains supplementary material, which is available to authorized users.

Worm variation made accessible: Take your shopping cart to store, link, and investigate!

In Caenorhabditis elegans, the recent advances in high-throughput quantitative analyses of natural genetic and phenotypic variation have led to a wealth of data on genotype phenotype relations. This data has resulted in the discovery of genes with major allelic effects and insights in the effect of natural genetic variation on a whole range of complex traits as well as how this variation is distributed across the genome. Regardless of the advances presented in specific studies, the majority of the data generated in these studies had yet to be made easily accessible, allowing for meta-analysis. Not only data in figures or tables but meta-data should be accessible for further investigation and comparison between studies. A platform was created where all the data, phenotypic measurements, genotypes, and mappings can be stored, compared, and new linkages within and between published studies can be discovered. WormQTL focuses on quantitative genetics in Caenorhabditis and other nematode species, whereas WormQTL^HD quantitatively links gene expression quantitative trait loci (eQTL) in C. elegans to gene–disease associations in humans.

HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors

MOTIVATION

Identification of expression Quantitative Trait Loci (eQTL), the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis.

METHODS

We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model is a combined multivariate regression and factor analysis, where the complete likelihood of the model is used to derive a ridge estimator for simultaneous factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects; it provides P-values and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of single nucleotide polymorphisms on a standard 8 core 2.6 G desktop.

RESULTS

By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and significantly outperforms all related hidden factor methods for eQTL analysis when there are eQTL with univariate and multivariate (pleiotropic) effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in the human lung for a study that included presumptive hidden factors. HEFT identified all of the cis-eQTL found by other hidden factor methods and 91 additional cis-eQTL. HEFT also identified a number of eQTLs with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer.

AVAILABILITY

Software is available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

WormQTLHD--a web database for linking human disease to natural variation data in C. elegans

Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism—Caenorhabditis elegans—has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTL^HD (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene–disease associations in man. WormQTL^HD, available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene–disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.

Cytotoxic T lymphocyte antigen-4 +49A/G polymorphism does not affect susceptibility to autoimmune hepatitis

BACKGROUND & AIMS

Single nucleotide polymorphisms (SNP) in the Cytotoxic T lymphocyte antigen-4 gene (CTLA-4) have been associated with several autoimmune diseases including autoimmune Hepatitis (AIH). In this chronic idiopathic inflammatory liver disease, conflicting results have been reported on the association with a SNP at position +49 in the CTLA-4 gene in small patient cohorts. Here, we established the role of this SNP in a sufficiently large cohort of AIH patients.

METHODS

The study population consisted of 672 AIH patients derived from academic and regional hospitals in the Netherlands and was compared with 500 controls selected from the 'Genome of the Netherlands' project cohort. Genotype frequencies were assessed by PCR for patients and by whole genome sequencing for controls.

RESULTS

No significant differences in allele frequencies were found between patients and controls (G Allele: 40% vs 39%, P = 0.7). Similarly, no significant differences in genotype frequencies between patients and controls were found. Finally, there was no relation between disease activity and the G allele or AG and GG genotypes.

CONCLUSION

The Cytotoxic T Lymphocyte Antigen-4 +49 A/G polymorphism does not represent a major susceptibility risk allele for AIH in Caucasians and is not associated with disease severity at presentation.

Contribution of rare and common variants determine complex diseases-Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common 'low penetrant' variants in combination with rare or private 'high penetrant' variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.

WormQTL--public archive and analysis web portal for natural variation data in Caenorhabditis spp

Here, we present WormQTL (http://www.wormqtl.org), an easily accessible database enabling search, comparative analysis and meta-analysis of all data on variation in Caenorhabditis spp. Over the past decade, Caenorhabditis elegans has become instrumental for molecular quantitative genetics and the systems biology of natural variation. These efforts have resulted in a valuable amount of phenotypic, high-throughput molecular and genotypic data across different developmental worm stages and environments in hundreds of C. elegans strains. WormQTL provides a workbench of analysis tools for genotype-phenotype linkage and association mapping based on but not limited to R/qtl (http://www.rqtl.org). All data can be uploaded and downloaded using simple delimited text or Excel formats and are accessible via a public web user interface for biologists and R statistic and web service interfaces for bioinformaticians, based on open source MOLGENIS and xQTL workbench software. WormQTL welcomes data submissions from other worm researchers.