Biology subjects

Reinders, M. J. T.

Publications and source records attributed to Reinders, M. J. T..

5 recordsLinked to original sources

bioRxiv2018

Centenarian Controls Increase Variant Effect-sizes by an average two-fold in an Extreme Case-Extreme Control Analysis of Alzheimer’s Disease

The detection of genetic loci associated with Alzheimers disease (AD) requires large numbers of cases and controls because variant effect-sizes are mostly small. We hypothesized that variant effect-sizes should increase when individuals who represent the extreme ends of a disease spectrum are considered, as their genomes are assumed to be maximally enriched or depleted with disease-associated genetic variants.\n\nWe used 1,073 extensively phenotyped AD cases with relatively young age at onset as extreme cases (66.3{+/-}7.9 years), 1,664 age-matched controls (66.0{+/-}6.5 years) and 255 cognitively healthy centenarians as extreme controls (101.4{+/-}1.3 years). We estimated the effect-size of 29 variants that were previously associated with AD in genome-wide association studies.\n\nComparing extreme AD-cases with centenarian-controls increased the variant effect-size relative to published effect-sizes by on average 1.90-fold (SE=0.29, p=9.0x10-4). The effect-size increase was largest for the rare high-impact TREM2 (R74H) variant (6.5-fold), and significant for variants in/near ECHDC3 (4.6-fold), SLC24A4-RIN3 (4.5-fold), NME8 (3.8-fold), PLCG2 (3.3-fold), APOE-{varepsilon}2 (2.2-fold) and APOE-{varepsilon}4 (2.0-fold). Comparing extreme phenotypes enabled us to replicate the AD association for 10 variants (p<0.05) in relatively small samples. The increase in effect-sizes depended mainly on using centenarians as extreme controls: the average variant effect-size was not increased in a comparison of extreme AD cases and age-matched controls (0.94-fold, p=6.8x10-1), suggesting that on average the tested genetic variants did not explain the extremity of the AD-cases. Concluding, using centenarians as extreme controls in AD case-controls studies boosts the variant effect-size by on average two-fold, allowing the replication of disease-association in relatively small samples.

genetics↗

bioRxiv2018

A structural equation model for imaging genetics using spatial transcriptomics

Alzheimers disease is a neurodegenerative disorder that causes changes in the structure of the brain, observable with MRI scans, and that has a strong heritable component, reflected in the DNA. Imaging genetics deals with such relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored both with dimension reduction methods and model based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting, and applied on a case study of the Alzheimers Disease Neuroimaging Initiative.

bioinformatics↗

bioRxiv2017

Variants in RNA-Seq data show a continued mutation rate during strain preservation of Schizophyllum commune

BackgroundTypical microorganism studies link genetic markers to physiological observations, like growth and survival. Experiments are carefully designed, comparing wildtype strains with knockout strains, and replications are conducted to capture biological variation. To maintain monoclonal strains, strain preservation systems are used to keep the number of generations between the primary stock and the experimental measurement low, to decrease the influence of spontaneous mutations on the experimental outcome. The impact of spontaneous mutations during the minimal number of growth cycles for the experimental design is, however, poorly studied.\n\nResultsWe set out to characterize the mutation landscape using a transcriptomic dataset of Schizophyllum commune, a laboratory model for mushroom formation. We designed a methodology to detect SNPs from the RNA-seq data, and found a mutation rate of 1.923 10-8 per haploid genome per base per generation, highly similar to the previously described mutation rate of S. commune in the wild. Our results imply that approximately 300 mutations are generated during growth of a colony on an agar plate, of which 5 would introduce stop codons. Knock-outs did not incur an increase of mutations and chromosomal recombination occurring at mating type loci was frequent. We found that missense and nonsense SNPs were selected against throughout the experiment. Also, most mutations show a low variant allele frequency and appear only in a small part of the population. Yet, we found 40 genes that gained a nonsense mutation affecting one of its annotated protein domains, and more than 400 genes having a missense mutation inside an annotated protein domain. Further, we found transcription factors, metabolic genes and cazymes having gained a mutation. Hence, the mutation landscape is wide-spread and has many functional annotations.\n\nConclusionsWe have shown that spontaneous mutations accumulate in typical microorganism experiments, where one usually assumes that these do not happen. As these mutations possibly confound experiments they should be minimized as much as possible, or, at least, be trackable. Therefore, we recommend labs to ensure that biological replicates originate from different parental plates, as much as possible.

genomics↗

bioRxiv2017

Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets.

bioinformatics↗

bioRxiv2017

Nucleus Specific Expression In The Multinucleated Mushroom-Forming Fungus Agaricus bisporus Reveals Different Nuclear Regulatory Programs

MotivationFungi are essential in nutrient recycling in nature. They also form symbiotic, commensal, parasitic and pathogenic interactions with other organisms including plants, animals and humans. Many fungi are polykaryotic, containing multiple nuclei per cell. In the case of heterokaryons, there are even different nuclear types within a cell. It is unknown what the different nuclear types contribute in terms of mRNA expression levels in fungal heterokaryons. Each cell of the cultivated, mushroom forming basidiomycete Agaricus bisporus contains 2 to 25 nuclei of two nuclear types, P1 or P2, that originate from two parental strains. Using RNA-Seq data, we wish to assess the differential mRNA contribution of individual nuclear types in heterokaryotic cells and its functional impact.\n\nResultsWe studied differential expression between genes of the two nuclear types throughout mushroom development of A. bisporus in various tissue types. The two nuclear types, produced specific mRNA profiles which changed through development of the mushroom. The differential regulation occurred at a gene and multi-gene locus level, rather than the chromosomal or nuclear level. Although the P1 nuclear type dominates the mRNA production throughout development, the P2 type showed more differentially upregulated genes in important functional groups including genes involved in metabolism and genes encoding secreted proteins. Out of 5,090 karyolelle pairs, i.e. genes with different alleles in the two nuclear types, 411 were differentially expressed, of which 246 were up-regulated by the P2 type. In the vegetative mycelium, the P2 nucleus up-regulated almost three-fold more metabolic genes and cazymes than P1, suggesting phenotypic differences in growth. A total of 10% of the differential karyollele expression is associated with differential methylation states, indicating that epigenetic mechanisms may be partly responsible for nuclear specific expression.\n\nConclusionWe have identified widespread transcriptomic variation between the two nuclear types of A. bisporus. Our novel method enables studying karyollelle specific expression which likely influences the phenotype of a fungus in a polykaryotic stage. This is thus relevant for the performance of these fungi as a crop and for improving this species for breeding. Our findings could have a wider impact to better understand fungi as pathogens. This work provides the first insight into the transcriptomic variation introduced by genomic nuclear separation.

microbiology↗