Biology subjects

Iwata, H.

Publications and source records attributed to Iwata, H..

36 records · Page 2Linked to original sources

bioRxiv2024

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BackgroundChemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. ResultsWe applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. ConclusionsWe believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

bioinformatics↗

bioRxiv2024

Development of a novel GWAS method to detect QTL effects interacting with the discrete and continuous population structure

Although GWAS has been a key technology to identify causal genes, the current standard GWAS model still has problems that need to be solved. Among them, the population structure is one of the most severe problems when detecting QTLs in GWAS since the GWAS model is statistically confounded by effects derived from the population structure. Further, the existence of QTLs, whose effects depend on the genetic background, also affects the conventional GWAS results by causing many false positives. Although the model to detect these population-specific QTLs has already been developed, this model requires prior information on the population structure, which may only sometimes be available. Also, the previous model only assumed the situation where QTLs interact with the discrete population structure. However, target populations of GWAS often consist of genetic resources with a more continuous population structure, and there has been no model that can consider such QTLs interacting with the continuous structure. In this study, by explicitly including an interaction term between a SNP/haplotype block and the genetic background in the conventional SNP-based/haplotype block-based GWAS model, we developed two models, named SNPxGB and HBxGB, that can detect QTLs interacting with the discrete and continuous structure. Our developed models were compared to the previous models by a simulation study assuming some types of QTLs, i.e., QTLs with effects common to all the backgrounds, specific to one genetic background, and interacting with polygenes. The simulation study showed that the models assuming the same situation as the simulation settings for each QTL type were suitable for detecting the corresponding QTLs. Primarily, our second HBxGB model could detect QTLs interacting with polygenes, i.e., continuous population structure, better than the previous model utilizing the prior population structure information. Our developed models are expected to help unravel the unknown genetic architecture of many complex traits. Author summaryGWAS aims at detecting candidate genes associated with a target trait via statistical testing. Since a classical GWAS starts with the constitution of a panel of individuals, usually gathered from different populations, many methods have been proposed to control the false positives in large datasets with a strong population structure. However, most methods assume the same QTL effect across populations, which is not always true in the natural biological process. One study has proposed a method to consider population-specific QTL effects by assuming marker effects depend on each subpopulation with prior information on population membership for each individual. This information on the population structure, however, may only sometimes be available, and sometimes the population structure is more continuous rather than discrete, where their methodology cannot be applied. We successfully developed two novel models that do not require prior knowledge of the population structure by explicitly including an interaction term between a SNP/haplotype block of interest and the genetic background in the conventional SNP-based/haplotype block-based GWAS model. The developed models, named SNPxGB and HBxGB, were suitable for capturing gene effects interacting with the discrete and continuous population structure, leading to the clarification of the genetic architecture of complex traits.

genomics↗

bioRxiv2023

Assessing the potential for genome-assisted breeding in red perilla using quantitative trait locus analysis and genomic prediction

Red perilla is an important medicinal plant used in Kampo medicine, and the development of elite varieties of this species is urgently necessary. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on the phenotypes of the compounds (i.e., conventional selection) is expensive and time-consuming. Here, we proposed genomic selection (GS) and marker-assisted selection (MAS) as suitable selection methods for medicinal plants, and evaluated the effectiveness of GS and MAS in red perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and to evaluate the accuracy of genomic prediction (GP). We found that GP had sufficiently high accuracy for all traits, confirming that GS was an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that use of these populations in breeding may yield simultaneous enhancements of different target traits. This study can contribute to research on the genetic mechanisms of the major medicinal compounds of red perilla as well as the breeding efficiency of this medicinal plant.

genomics↗

bioRxiv2023

An integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data

High-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics microbiome data has been proposed. In this study, we propose a novel framework called integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data. The I-SVVS approach addresses a specific Bayesian mixture model for each type of omics data, such as an infinite Dirichlet multinomial mixture model for microbiome data and an infinite Gaussian mixture model for metabolomic data. This approach is expected to reduce the computational time of the clustering process and improve the accuracy of the clustering results. Additionally, I-SVVS identifies a critical set of representative variables in multi-omics microbiome data. Three datasets from soybean, mice, and humans (each set integrated microbiome and metabolome) were used to demonstrate the potential of I-SVVS. The results indicate that I-SVVS achieved improved accuracy and faster computation compared to existing methods across all test datasets. It effectively identified key microbiome species and metabolites characterizing each cluster. For instance, the computational analysis of soybean dataset, including 377 samples with 16,943 microbiome species and 265 metabolome features, was completed in 2.18 hours using I-SVVS, compared to 2.35 days with Clusternomics and 1.12 days with iClusterPlus. The software for this analysis, written in Python, is freely available at https://github.com/tungtokyo1108/I-SVVS.

bioinformatics↗

bioRxiv2023

Modeling soybean growth: A mixed model approach

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics. Author summaryNonlinear models are useful for modeling animal and plant growth; however, their parameters are influenced by both genetic and environmental factors. If the same model can be applied to data with different genetic and environmental factors by allowing parameter variations, it can be used to understand, predict, and control the genetic and environmental influences of growth models based on parameter variation. In this study, we propose a statistical method based on nonlinear mixed-effects modeling. The simulation and real data analysis results show that the proposed method was effective in modeling the growth of genetically different soybean varieties under different drought conditions. The usefulness of the proposed method is expected to increase, as high-throughput measurements provide growth data for a large number of genotypes in various environments.

genetics↗

bioRxiv2023

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

This study investigated a method to evaluate the drought tolerance stability of a genotype in a single environmental trial by capturing the plant response to irrigation changes. Genotypes that exhibit stable phenotypes under various drought stress conditions are required for stable crop production. However, considerable time and money are required to evaluate the environmental stability of a genotype through multiple environmental trials. As an index of drought tolerance stability, we calculated the coefficient of variation (CV) of shoot fresh weight of 178 soybean (Glycine max (L.) Merr.) accessions in a total of nine types of drought treatments, including changing irrigation treatments (every five or ten days) over 3-year trials. To capture the plant responses to irrigation changes, time-series multispectral (MS) data were collected, including the timings of the irrigation/non-irrigation switch in the changing irrigation treatments. We built a random regression model (RRM) for each of the nine treatments using the time-series MS data. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In two out of the three years, the prediction accuracy of MTRRM models built in the changing irrigation treatment was higher than that in the continuous drought treatment in the same year. When the CV was predicted using the MTRRM model across years in the changing irrigation treatment, the prediction accuracy was 61% higher than that of the simple genomic prediction model. These results suggest that drought tolerance stability can be evaluated in a single environmental trial, which may reduce the time and cost of selecting genotypes with high drought tolerance stability.

genetics↗

bioRxiv2023

Simulation-based future-oriented strategy can optimize decision making in a breeding program

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function of multiple selection criteria. Different selection criteria are weighted in the function to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

genomics↗

bioRxiv2021

Stochastic variational variable selection for high-dimensional microbiome data

BackgroundThe rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the framework of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. ResultsWe proposed a novel framework to improve the performance of the widely used DMM approach by combining three ideas: (i) we extended the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a random variables; (ii) we proposed an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; and (iii) to address the computational burden of high-dimensional microbiome data, we proposed a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. ConclusionsSVVS demonstrated a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1,000 samples. Furthermore, recent microbiome studies have suggested that selection of the microbial species used as a core set is important.

bioinformatics↗

bioRxiv2021

Dominant Awn Inhibitor, which encodes an ALOG protein in sorghum, suppresses awn formation in rice

The awn, a needle-like structure extending from the tip of the lemma in grass species, plays a role in environmental adaptation and fitness. In some crops, awns appear to have been eliminated during domestication. Although numerous genes involved in awn development have been identified, several dominant genes that eliminate awns are also known to exist. For example, in sorghum (Sorghum bicolor), the dominant awn inhibiting gene has been known since 1921; however, its molecular features remain uncharacterized. In this study, we conducted quantitative trait locus analysis and a genome-wide association study of awn-related traits in sorghum and identified DOMINANT AWN INHIBITOR (DAI), which encodes the ALOG family protein on chromosome 3. DAI appeared to be present in most awnless sorghum cultivars, likely because of its effectiveness. Detailed analysis of the ALOG protein family in cereals revealed that DAI originated from duplication of its twin paralog (DAIori) on chromosome 10. Observations of immature awns in near-isogenic lines revealed that DAI inhibits awn elongation by suppressing both cell proliferation and elongation. We also found that only DAI gained a novel function to inhibit awn elongation through an awn-specific expression pattern distinct from that of DAIori. Interestingly, heterologous expression of DAI with its own promoter in rice inhibited awn elongation in the awned cultivar Kasalath. We found that DAI originated from gene duplication, providing an interesting example of gain-of-function that occurs only in sorghum but shares its functionality with rice and sorghum.

plant biology↗

bioRxiv2021

Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

plant biology↗

bioRxiv2021

Therapeutic base and prime editing of COL7A1 mutations in recessive dystrophic epidermolysis bullosa

Recessive dystrophic epidermolysis bullosa (RDEB) is a severe skin fragility disorder caused by loss-of-function mutations in the COL7A1 gene, which encodes type VII collagen (C7), a protein that functions in skin adherence. From 36 Korean RDEB patients, we identified a total of 69 pathogenic mutations (40 variants without recurrence), including point mutations (72.5%) and insertion/deletion mutations (27.5%). We used base and prime editing to correct mutations in fibroblasts from two patients (Pat1, who carried a c.3631C>T mutation in one allele, and Pat2, who carried a c.2005C>T mutation in one allele). We applied adenine base editors (ABEs) to correct the pathogenic mutation or to bypass a premature stop codon in Pat1-derived primary fibroblasts. To expand the targeting scope, we also utilized prime editors (PEs) to correct the mutations in Pat1- and Pat2-derived fibroblasts. Ultimately, we found that both ABE- and PE-mediated correction of COL7A1 mutations restored full-length C7 expression, reversed the impaired adhesion and proliferation exhibited by the patient-derived fibroblasts, and, following transfer of edited patient-derived fibroblasts into the skin of immunodeficient mice, led to C7 deposition within the dermal-epidermal junction. These results suggest that base and prime editing could be feasible strategies for ex vivo gene editing to treat RDEB.

bioengineering↗

bioRxiv2021

Simple amplicon sequencing library preparation for plant root microbial community profiling

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have developed a novel high-throughput method based on a two-step PCR amplification protocol, involving DNA extraction using magnetic beads and PCR purification using exonuclease, for 16S rRNA gene amplicon sequencing of plant root microbiota. This method reduces sample handling and captures microbial diversity comparable to that obtained by the standard method. We found that using a buffer with magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. In addition, we demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. Our method offers a simple and high-throughput solution for maintaining the quality of plant root microbial community profiling.

plant biology↗

bioRxiv2020

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs

Key messageWe propose a novel approach to the Bayesian optimization of multi-variate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about 20% of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.

genomics↗

bioRxiv2020

Subepidermal blisters are healed by wedge-shaped keratinocytes of hair follicle origin

Injury in adult tissue generally reactivates developmental programs to foster regeneration, but it is not known whether this paradigm applies to growing tissue. Here, by employing blisters, we show that epidermal wounds heal at the expense of skin development. The regenerated epidermis suppresses the expression of tissue morphogenesis genes accompanied by delayed hair follicle (HF) growth. Lineage tracing experiments, cell proliferation dynamics, and mathematical modeling reveal that the progeny of HF junctional zone stem cells, which undergo a morphological transformation, repair the blisters while not promoting HF development. In contrast, the contribution of interfollicular stem cell progeny to blister healing is small. These findings demonstrate that tissue development can be sacrificed for the sake of wound regeneration and suggest that tissue repair does not coincide with the reactivation of developmental programs in all regenerative contexts. Our study elucidates the key cellular mechanism of wound healing in skin blistering diseases.

cell biology↗

bioRxiv2019

Choosing the optimal population for a genome-wide association study: a simulation using whole-genome sequences from rice

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS, e.g. population structure, sample size, and sequence analysis and field testing costs, need to be considered. Mixture populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, e.g., a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversities. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS power with a mixture population was generally higher than with a separate population. Also, the GWAS optimal population varied depending on the fixation index FST of the quantitative trait nucleotide (QTN) and its polymorphism of QTN in each population. When a QTN is polymorphic in a target population, a target population combined with a higher diversity population improves the QTN detection power. Investigating FST and the expected heterozygosity He as factors influencing the detection power, we showed that SNPs with high FST or low He are less likely to be detected by GWAS with mixture populations. Sequenced/genotyped germplasm collections can improve the GWAS detection power by using a subset of them with a target population.\n\nCore ideas (3-5 impact statements, 85 char max for each)\n\n- Genome-wide association studies with mixture populations are expected to improve the detection power of novel genes due to the increase of the sample size although the influence of population structure is a concern.\n- When a quantitative trait nucleotide (QTN) is polymorphic in a target population, a combination of the target population and a population with higher diversity than the target population improves the detection power of the QTN.\n- We found that the fixation index (FST) and the expected heterozygosity (He) were strongly related to the detection power of QTNs.\n- Germplasm collections which have been already sequenced/genotyped are useful for improving the detection power of GWAS without any addition of sequence costs by using a subset of them with a target population.

genomics↗

bioRxiv2019

Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages) this implementation might not be the most convenient. GS might deliver high rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with significant impact in yield potential. The objectives of this research consisted in the development of a method that accurately predicts time related traits like DTH in unobserved environments. For this, we developed an implementation that incorporate day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned an root-mean-square-error (RMSE) of 3.9 days, a Pearson Correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.

genomics↗

bioRxiv2019

Tuning intercellular cohesion with membrane-anchored oligonucleotides

Cohesive interactions between cells play an integral role in development, differentiation, and regeneration. Existing methods for controlling cell-cell cohesion by manipulating protein expression are constrained by biological interdependencies, e.g. coupling of cadherins to actomyosin force-feedback mechanisms. We use oligonucleotides conjugated to PEGylated lipid anchors (ssDNAPEGDPPE) to introduce artificial cell-cell cohesion that is largely decoupled from the internal cytoskeleton. We describe cell-cell doublets with a mechanical model based on isotropic, elastic deformation of spheres to estimate the cohesion at the cell-cell interface. Physical manipulation of cohesion by modulating PEG-lipid to ssDNAPEGDPPE ratio, and conversely treatment with actin-depolymerizing cytochalsin-D, resulted respectively in decreases and increases in doublet contact area. Our data are relevant to the ongoing discussion over mechanisms of tissue surface tension and in agreement with models based on opposing cortical and cohesive forces. PEG-lipid modulation of doublet geometries resulted in a well-defined curve indicating continuity, enabling prescriptive calibration for controlling doublet geometry. Our study demonstrates tuning of basic doublet cohesion, laying the foundation for more complex multicellular cohesion control independent of protein expression.

biophysics↗

bioRxiv2019

Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW

BackgroundDiffculty in detecting rare variants is one of the problems in conventional genome wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set GWAS method named \"RAINBOW\" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes.\n\nResultsWe prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. The results of the comparison indicated that the proposed method was able to better control false positives than the others. The proposed method was also excellent at detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset. Moreover, the proposed method showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, especially when the causal variants were located very close to each other and the directions of their effects were opposite.\n\nConclusionThe proposed method, RAINBOW, is especially superior in controlling false positives, detecting causal variants, and detecting nearby causal variants with opposite effects. By using the SNP-set approach as the proposed method, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as the R package and is available at https://github.com/KosukeHamazaki/RAINBOW.

genetics↗