Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Validation of a Miniaturized Permeability Assay Compatible with CRISPR-Mediated Genome-Wide Screen

The impermeability of the luminal endothelial cell monolayer is crucial for the normal performance of the vascular and lymphatic systems. A key to this function is the integrity of the monolayer's intercellular junctions. The known repertoire of junction-regulating genes is incomplete. Current permeability assays are incompatible with high-throughput genome-wide screens that could identify these genes. To overcome these limitations, we designed a new permeability assay that consists of cell monolayers grown on ~150 μm microcarriers (MCs). Each MC functions as a miniature individual assay of permeability (MAP). We demonstrate that false-positive results can be minimized, and that MAP sensitivity to thrombin-induced increase in monolayer permeability is similar to the sensitivity of impedance measurement. We validated the assay by showing that the expression of single guide RNAs (sgRNAs) that target genes encoding known thrombin signaling proteins blocks effectively thrombin-induced junction disassembly, and that MAPs carrying such cells can be separated effectively by fluorescence-assisted sorting from those that carry cells expressing non-targeting sgRNAs. These results indicate that MAPs are suitable for high-throughput experimentation and for genome-wide screens for genes that mediate the disruptive effect of thrombin on endothelial cell junctions.

Rare autosomal trisomies: comparison of detection through cell-free DNA analysis and direct chromosome preparation of chorionic villus samples

OBJECTIVE

Direct chromosome preparations of chorionic villus samples (CVS) and cell-free DNA (cfDNA) testing both involve analysis of the trophoblastic cell lineage. The aim of this study was to compare the spectrum of rare autosomal trisomies (RATs) detected by these two approaches and assess the available information on their clinical significance.

METHODS

Data from 10 reports on genome-wide cfDNA testing were pooled to determine which chromosomes were most frequently involved in RAT-positive cases, and pregnancy outcome information was reviewed. CVS information was obtained from an updated database of 76 102 consecutive CVS analyses performed over a period of 18 years at TOMA laboratory, in which trophoblastic and mesenchymal layers were analyzed and amniotic fluid cell analysis was recommended for RAT-positive cases. Chromosomes involved and presence of confined placental mosaicism, true fetal mosaicism and uniparental disomy (UPD) for imprinted chromosomes were assessed. Also evaluated were the frequency and types of RATs in products of conception.

RESULTS

RATs were present in 634 of 196 662 (0.32%) cfDNA samples and 237 of 57 539 (0.41%) CVS trophoblast samples (P < 0.01). The frequency of RATs varied over 8-fold between the cfDNA reports. Confirmation of abnormality through amniocentesis was more likely when RATs were ascertained through cfDNA (14 of 151; 9.3%) than through CVS trophoblasts (seven of 237; 3.0%) (P < 0.01). In cfDNA-ascertained cases, trisomies 15, 16 and 22, which are associated with fetal loss, were identified proportionately more often. Of 151 cases with RAT identified by cfDNA and outcome information available, 41.1% resulted in normal live birth; 27.2% in fetal loss; 7.3% had phenotypic abnormality detected through ultrasound or other follow-up evaluation; 2.0% had a clinically significant UPD; and 14.6% had fetal growth restriction or low birth weight. All autosomes were involved in trisomies in products of conception; the most common RATs detected were trisomies 16, 22 and 15 with a frequency of > 9% each.

CONCLUSIONS

Although there are strong parallels between RATs ascertained through cfDNA analysis and direct chromosome preparation of CVS, caution is needed in applying conclusions from CVS analysis to cfDNA testing, and vice versa. RATs identified through genome-wide cfDNA tests have uncertain risks for fetal loss, growth restriction or fetal abnormality. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease

Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.

Microfluidic genome-wide profiling of intrinsic electrical properties in Saccharomyces cerevisiae

Methods to analyze the intrinsic physical properties of cells - for example, size, density, rigidity, or electrical properties - are an active area of interest in the microfluidics community. Although the physical properties of cells are determined at a fundamental level by gene expression, the relationship between the two remains exceptionally complex and poorly characterized, limiting the adoption of intrinsic separation technologies. To improve our current understanding of how a cell's genotype maps to a measurable physical characteristic and quantitatively investigate the potential of using these characteristics as biomarkers, we have developed a novel screen that combines microfluidic cell sorting with high-throughput sequencing and the haploid yeast deletion library to identify genes whose functions modulate one such characteristic - intrinsic electrical properties. Using this screen, we are able to establish a high-content electrical profile of the haploid yeast gene deletion strains. We find that individual genetic deletions can appreciably alter the electrical properties of cells, affecting ~10% of the 4432 gene deletion strains screened. Additionally, we find that gene deletions affecting electrical properties in specific ways (i.e. increasing or decreasing effective conductivity at higher or lower electric field frequencies) are strongly associated with an enriched subset of fundamental biological processes that can be traced to specific pathways and complexes. The screening approach demonstrated here and the attendant results are immediately applicable to the intrinsic separations community.

Parallel imaging microfluidic cytometer

By adding an additional degree of freedom from multichannel flow, the parallel microfluidic cytometer (PMC) combines some of the best features of fluorescence-activated flow cytometry (FCM) and microscope-based high-content screening (HCS). The PMC (i) lends itself to fast processing of large numbers of samples, (ii) adds a 1D imaging capability for intracellular localization assays (HCS), (iii) has a high rare-cell sensitivity, and (iv) has an unusual capability for time-synchronized sampling. An inability to practically handle large sample numbers has restricted applications of conventional flow cytometers and microscopes in combinatorial cell assays, network biology, and drug discovery. The PMC promises to relieve a bottleneck in these previously constrained applications. The PMC may also be a powerful tool for finding rare primary cells in the clinic. The multichannel architecture of current PMC prototypes allows 384 unique samples for a cell-based screen to be read out in ∼6-10 min, about 30 times the speed of most current FCM systems. In 1D intracellular imaging, the PMC can obtain protein localization using HCS marker strategies at many times for the sample throughput of charge-coupled device (CCD)-based microscopes or CCD-based single-channel flow cytometers. The PMC also permits the signal integration time to be varied over a larger range than is practical in conventional flow cytometers. The signal-to-noise advantages are useful, for example, in counting rare positive cells in the most difficult early stages of genome-wide screening. We review the status of parallel microfluidic cytometry and discuss some of the directions the new technology may take.

Technologies for diabetes genomics

The genetic risk for diabetes largely depends on the type of diabetes and the penetrance and severity of the effect of the contributing genes. This ranges from the high-risk mutations of neonatal diabetes and maturityonset diabetes of the young to the lower, but still significant, risk conferred by common human leukocyte antigen alleles in type 1 diabetes to the still-lower risk conferred by the common variants associated with type 2 diabetes. There are many new molecular technologies, each with their own set of methodological issues, that have been used for genome-wide association studies and that can be used for determining the genetic risk for these various types of diabetes. These technologies include whole genome single nucleotide polymorphism microarrays, high-throughput polymorphism analyzers, next-generation sequencers, and copy-number variant technologies.

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.