Ensemble learning for integrative prediction of genetic values with genomic variants.

BACKGROUND: Whole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others. RESULTS: We herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesCπ, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E-118 to 9.640E-20 for WTCCC dataset. CONCLUSIONS: ELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.

Optimizing genomic control in mixed model associations with binary diseases.

Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. Further, joint association analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

Hierarchical mixed-model expedites genome-wide longitudinal association analysis.

A hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.

Genome-wide hierarchical mixed model association analysis.

In genome-wide mixed model association analysis, we stratified the genomic mixed model into two hierarchies to estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and statistically infer the association of GBVs with each SNP using the generalized least square. The hierarchical mixed model (Hi-LMM) can correct confounders effectively with polygenic effects as residuals for association tests, preventing potential false-negative errors produced with genome-wide rapid association using mixed model and regression or an efficient mixed-model association expedited (EMMAX). Meanwhile, the Hi-LMM performs the same statistical power as the exact mixed model association and the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, the Hi-LMM can detect more quantitative trait nucleotides (QTNs) than existing methods. Especially under the Hi-LMM framework, joint association analysis can be made straightforward to improve the statistical power of detecting QTNs.

Low-Pressure Sensitive Piezochromic Fluorescence Switching of Tetraphenylethylene-Anthraquinone.

Invited for the cover of this issue are Prof. Wenjing Tian and co-workers at Jilin University. The image depicts the highly sensitive piezochromic fluorescence switching of tetraphenylethylene-anthraquinone under low-pressure regimes (∼60 kPa). Read the full text of the article at 10.1002/chem.202301070.

Low-Pressure Sensitive Piezochromic Fluorescence Switching of Tetraphenylethylene-Anthraquinone.

Sensing of low-pressure signals is of great importance for cutting-edge technologies. Organic piezochromic molecules offer a promising library of pressure sensitive materials which can be tailor-designed toward specific requirements. However, very few examples of low-pressure sensitive piezochromic fluorescent molecules have been obtained till date, and the underlying mechanisms are still in its infancy. Herein, we report highly sensitive piezochromic fluorescent switching under low-pressure regimes (∼60 kPa) of tetraphenylethylene-anthraquinone (TPE-AQ) based on the controlled molecular design and polymorphic phase strategy. The influence of both intramolecular conformation effect and variations of intermolecular stacking modes on the piezochromic property of TPE-AQ is investigated. The underlying mechanism of the low-pressure sensitive piezochromic fluorescence switching is demonstrated to be closely related to the loosely packed molecular orientation, as confirmed by X-ray diffraction measurements combined with simulations. This work provides a way to design highly efficient pressure sensors based on organic molecular systems.

Canonical transformation for multivariate mixed model association analyses.

KEY MESSAGE: In extension of Single-RunKing to analyze multiple correlated traits, mvRunKing not only enlarged number of the analyzed phenotypes with canonical transformation, but also improved statistical power to detect pleiotropic QTNs through joint association analysis. Based on genomic variance-covariance matrices, we simplified multivariate mixed model association analysis to multiple univariate ones by using canonical transformation, and then individually implemented univariate association tests in the Single-RunKing. which enlarged number of the analyzed phenotypes. With canonical transformation back to the original scale, the association results would be biologically interpretable. Especially, we rapidly estimated genomic variance-covariance matrices with multivariate GEMMA and optimized separately the polygenic variances (or heritabilities) for only the markers that had large effects or higher significance levels in univariate mixed models, greatly improving computing efficiency for multiple univariate association tests. Beyond one test at once, joint association analysis for quantitative trait nucleotide (QTN) candidates can significantly increase statistical powers to detect QTNs. A user-friendly mvRunKing software was developed to efficiently implement multivariate mixed model association analyses.

The deubiquitinase CYLD controls protective immunity against helminth infection by regulation of Treg cell plasticity.

BACKGROUND: Type 2 immunity can be modulated by regulatory T (Treg) cell activity. It has been suggested that the deubiquitinase cylindromatosis (CYLD) plays a role in the development or function of Treg cells, implying that it could be important for normal protective immunity, where type 2 responses are prevalent. OBJECTIVE: We sought to investigate the role of CYLD in Treg cell function and TH2 cell immune responses under steady-state conditions and during helminth infection. METHODS: Foxp3-restricted CYLD conditional knockout (KO) mice were examined in mouse models of allergen-induced airway inflammation and Nippostrongylus brasiliensis infection. We performed multiplex magnetic bead assays, flow cytometry, and quantitative PCR to understand how a lack of CYLD affected cytokine production, homing, and suppression in Treg cells. Target genes regulated by CYLD were identified and validated by microarray analysis, coimmunoprecipitation, short hairpin RNA knockdown, and transfection assays. RESULTS: Treg cell-specific CYLD KO mice showed severe spontaneous pulmonary inflammation with increased migration of Treg cells into the lung. CYLD-deficient Treg cells furthermore produced high levels of IL-4 and failed to suppress allergen-induced lung inflammation. Supporting this, the conditional KO mice displayed enhanced protection against N brasiliensis infection by contributing to type 2 immunity. Treg cell conversion into IL-4-producing cells was due to augmented mitogen-activated protein kinase and nuclear factor κB signaling. Moreover, Scinderin, a member of the actin-binding gelsolin family, was highly upregulated in CYLD-deficient Treg cells, and controlled IL-4 production through forming complexes with mitogen-activated protein kinase kinase/extracellular receptor kinase. Correspondingly, both excessive IL-4 production in vivo and the protective role of CYLD-deficient Treg cells against N brasiliensis were reversed by Scinderin ablation. CONCLUSIONS: Our findings indicate that CYLD controls type 2 immune responses by regulating Treg cell conversion into TH2 cell-like effector cells, which potentiates parasite resistance.

Reversible Three-Color Fluorescence Switching of an Organic Molecule in the Solid State via "Pump-Trigger" Optical Manipulation.

In photoswitches that undergo fluorescence switching upon ultraviolet irradiation, photoluminescence and photoisomerization often occur simultaneously, leading to unstable fluorescence properties. Here, we successfully demonstrated reversible solid-state triple fluorescence switching through "Pump-Trigger" multiphoton manipulation. A novel fluorescence photoswitch, BOSA-SP, achieved green, yellow, and red fluorescence under excitation by pump light and isomerization induced by trigger light. The energy ranges of photoexcitation and photoisomerization did not overlap, enabling appropriate selection of the multiphoton light for "pump" and "trigger" photoswitching, respectively. Additionally, the large free volume of the spiropyran (SP) moiety in the solid state promoted reversible photoisomerization. Switching between "pump" and "trigger" light is useful for three-color tunable switching cell imaging, which can be exploited in programmable fluorescence switching. Furthermore, we exploited reversible dual-fluorescence switching in a single molecular system to successfully achieve two-color super-resolution imaging.

Quick approximation of threshold values for genome-wide association studies.

Standard normal statistics, chi-squared statistics, Student's t statistics and F statistics are used to map quantitative trait nucleotides for both small and large sample sizes. In genome-wide association studies (GWASs) of single-nucleotide polymorphisms (SNPs), the statistical distributions depend on both genetic effects and SNPs but are independent of SNPs under the null hypothesis of no genetic effects. Therefore, hypothesis testing when a nuisance parameter is present only under the alternative was introduced to quickly approximate the critical thresholds of these test statistics for GWASs. When only the statistical probabilities are available for high-throughput SNPs, the approximate critical thresholds can be estimated with chi-squared statistics, formulated by statistical probabilities with a degree of freedom of two. High similarities in the critical thresholds between the accurate and approximate estimations were demonstrated by extensive simulations and real data analysis.

A fast-linear mixed model for genome-wide haplotype association analysis: application to agronomic traits in maize.

BACKGROUND: Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS). In the haplotype association analysis, both haplotype alleles and blocks are tested. Haplotype alleles can be inferred with the same statistics as SNPs in the linear mixed model, while blocks require the formulation of unified statistics to fit different genetic units, such as SNPs, haplotypes, and copy number variations. RESULTS: Based on the FaST-LMM, the fastLmPure function in the R/RcppArmadillo package has been introduced to speed up genome-wide regression scans by a re-weighted least square estimation. When large or highly significant blocks are tested based on EMMAX, the genome-wide haplotype association analysis takes only one to two rounds of genome-wide regression scans. With a genomic dataset of 541,595 SNPs from 513 maize inbred lines, 90,770 haplotype blocks were constructed across the whole genome, and three types of markers (SNPs, haplotype alleles, and haplotype blocks) were genome-widely associated with 17 agronomic traits in maize using the software developed here. CONCLUSIONS: Two SNPs were identified for LNAE, four haplotype alleles for TMAL, LNAE, CD, and DTH, and only three blocks reached the significant level for TMAL, CD, and KNPR. Compared to the R/lm function, the computational time was reduced by ~ 10-15 times.

Diatoms as cell factories for high-value products: chrysolaminarin, eicosapentaenoic acid, and fucoxanthin.

Diatoms are unicellular photosynthetic microalgae existing ubiquitously in marine and freshwater environments. This review focuses on high-value compounds produced from diatoms, including chrysolaminarin (Chrl), eicosapentaenoic acid (EPA), and fucoxanthin (Fx), which can be applied in aquaculture, human health foods, pharmaceuticals, and cosmetics. In addition, this review provides an overview of their biosynthesis in diatoms and technologies for production. EPA and Fx typically accumulate synergistically in diatoms, while Chrl competes with EPA and Fx for carbon precursors. Several diatom strains have been employed that simultaneously accumulate these three compounds, but limitations and challenges still exist during commercialization. To address the bottleneck in biomass and high-value compound production, the optimization of cultivation parameters, the trophic mode, elicitor- or bacteria-assisted stimulations, and genetic modifications via mutant breeding, adaptive evolution engineering, and metabolic engineering have been developed in diatoms to establish improved technologies. Currently, large-scale cultivation of diatoms occurs mostly in open ponds and photobioreactors in autotrophic mode. Mixotrophic cultivation and coextraction approaches for multiple products represent novel strategies for economically enhancing the future production of biomass and high-value compounds on an industrial scale.

Genome-wide barebones regression scan for mixed-model association analysis.

KEY MESSAGE: Based on the simplified FaST-LMM, wherein genomic variance is replaced with heritability, we have significantly improved computational efficiency by implementing rapid R/fastLmPure to statistically infer the genetic effects of tested SNPs and focus on large or highly significant SNPs obtained using the EMMAX algorithm. For a genome-wide mixed-model association analysis, we introduce a barebones linear model fitting function called fastLmPure from the R/RcppArmadillo package for the rapid estimation of single nucleotide polymorphism (SNP) effects and the maximum likelihood values of factored spectrally transformed linear mixed models (FaST-LMM). Starting from the estimated genomic heritability of quantitative traits under a null model without quantitative trait nucleotides, maximum likelihood estimations of the polygenic heritabilities of candidate markers consume the same time as approximately four rounds of genome-wide regression scans. When focusing only on SNPs with large effects or high significance levels, as estimated by the efficient mixed-model association expedited algorithm, the run time of genome-wide mixed-model association analysis is reduced to at most two rounds of genome-wide regression scans. We have developed a novel software application called Single-RunKing to transform nonlinear mixed-model association analyses into barebones linear regression scans. Based on a realised relationship matrix calculated using genome-wide markers, Single-RunKing saves significantly computation time, as compared with the FaST-LMM that optimises the variance ratios of polygenic variances to residual variances using the R/lm function.

Influence of epistatic segregation distortion loci on genetic marker linkages in Japanese flounder.

For genetic linkage analysis of Japanese flounder, 160 doubled haploids (DH) were artificially produced using mitotic gynogenesis and were genotyped for 458 simple sequence repeat (SSR) markers, 101 of which show distortional segregation. The genetic linkage map was constructed by modifying recombination fractions between the distorted markers. Between the corrected and uncorrected genetic maps, there were considerable differences in genetic distance, but not in relative locations among markers. Using a liability model, a segregation distortion locus (SDL), with an additive genetic effect of 1.772, was mapped between markers BDHYP387 and Poli56TUF of chromosome 24 in the corrected genetic map. Additionally, six pairs of epistatic SDLs were identified on chromosomes 1, 5, 8, 9, 23, and 24. Changes in genetic distances between markers did not occur on chromosome regions with main effect SDLs. However, most chromosome regions where genetic distances changed covered the detected epistatic SDLs. This study concluded that epistatic SDLs decrease linkages between markers and lengthen genetic distances in Japanese flounder. This finding has been partially validated in other DH populations derived from three female Japanese flounders.

Identification and Characterization of MiRNAs in Coccomyxa subellipsoidea C-169.

Coccomyxa subellipsoidea C-169 (C-169) is an oleaginous microalga which is promising for renewable biofuel production. MicroRNAs (miRNAs), as the pivotal modulators of gene expression at post-transcriptional level, are prospective candidates for bioengineering practice. However, so far, no miRNA in C-169 has been reported and its potential impact upon CO2 supplementation remains unclear. High-throughput sequencing of small RNAs from C-169 cultured in air or 2% CO2 revealed 124 miRNAs in total, including 118 conserved miRNAs and six novel ones. In total, 384 genes were predicted as their potential target genes, 320 for conserved miRNAs and 64 for novel miRNAs. The annotated target genes were significantly enriched in six KEGG pathways, including pantothenate and CoA biosynthesis, C5-branched dibasic acid metabolism, 2-oxocarboxylic acid metabolism, butanoate metabolism, valine, leucine and isoleucine biosynthesis and alpha-linolenic acid metabolism. The miRNAs' target genes were enriched in lipid metabolism as well as RNA-interacting proteins involved in translation, transcription and rRNA processing. The pioneering identification of C-169 miRNAs and analysis of their putative target genes lay the foundation for further miRNA research in eukaryotic algae and will contribute to the development of C-169 as an oleaginous microalga through bioengineering in the future.

Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis.

Metaproteomics provides a direct measure of the functional information by investigating all proteins expressed by a microbiota. However, due to the complexity and heterogeneity of microbial communities, it is very hard to construct a sequence database suitable for a metaproteomic study. Using a public database, researchers might not be able to identify proteins from poorly characterized microbial species, while a sequencing-based metagenomic database may not provide adequate coverage for all potentially expressed protein sequences. To address this challenge, we propose a metagenomic taxonomy-guided database-search strategy (MT), in which a merged database is employed, consisting of both taxonomy-guided reference protein sequences from public databases and proteins from metagenome assembly. By applying our MT strategy to a mock microbial mixture, about two times as many peptides were detected as with the metagenomic database only. According to the evaluation of the reliability of taxonomic attribution, the rate of misassignments was comparable to that obtained using an a priori matched database. We also evaluated the MT strategy with a human gut microbial sample, and we found 1.7 times as many peptides as using a standard metagenomic database. In conclusion, our MT strategy allows the construction of databases able to provide high sensitivity and precision in peptide identification in metaproteomic studies, enabling the detection of proteins from poorly characterized species within the microbiota.

Immune Regulation by Ubiquitin Tagging as Checkpoint Code.

The immune system is equipped with effective machinery to mobilize its activation to defend invading microorganisms, and at the same time, to refrain from attacking its own tissues to maintain immune tolerance. The balance of activation and tolerance is tightly controlled by diverse mechanisms, since breakdown of tolerance could result in disastrous consequences such as the development of autoimmune diseases. One of the mechanisms is by the means of protein ubiquitination, which involves the process of tagging a small peptide ubiquitin to protein substrates. E3 ubiquitin ligases are responsible for catalyzing the final step of ubiquitin-substrate conjugation by specifically recognizing substrates to determine their fates of degradation or functional modification. The ubiquitination process is reversible, which is carried out by deubiquitinating enzymes to release the ubiquitin molecule from the conjugated substrates. Protein ubiquitination and deubiquitination serve as checkpoint codes in many key steps of lymphocyte regulation including the development, activation, differentiation, and tolerance induction. In this chapter, we will discuss a few E3 ligases and deubiquitinating enzymes that are important in controlling immune responses, with emphasis on their roles in T cells.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

BACKGROUND: Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. METHODS: We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. CONCLUSIONS: In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Iteratively reweighted LASSO for mapping multiple quantitative trait loci.

The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on Bayesian theory, whereas under a single QTL model, IRLS has very limited statistical power to detect multiple QTLs. In this study, we proposed the iteratively reweighted least absolute shrinkage and selection operator (IRLASSO) for extending IRLS to simultaneously map multiple QTLs. The LASSO with coordinate descent step is employed to efficiently estimate non-zero genetic effect of each locus scanned over entire genome. Simulations demonstrate that IRLASSO has a higher precision of parameter estimation and power to detect QTL than IRLS, and is able to estimate residual variance more accurately than the unweighted LASSO based on LS. Especially, IRLASSO is very fast, usually taking less than five iterations to converge. The barley dataset from the North American Barley Genome Mapping Project is reanalyzed by our proposed method.

An efficient approach to large-scale genotype-phenotype association analyses.

Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait, least absolute shrinkage and selection operator with coordinate descent step can be employed to efficiently shrink sparse non-zero genetic effects of quantitative trait loci (QTLs). However, regression analyses in a trait-by-trait basis do not take account of the correlations among the analyzed traits. In this study, conditional phenotype of each trait is defined, given other traits. Large-scale genotype-phenotype association analyses are therefore transformed to separate genotype-conditional phenotype ones. Meanwhile, the correlation architecture between each trait and other traits can also be provided by shrinkage estimation for each conditional phenotype. Simulation demonstrates that the proposed conditional mapping method is generally identical to joint mapping method based on multivariate analysis in terms of statistical detection power and parameter estimation. Application of the method is provided to locate eQTL in yeast.