The use of a genetic relationship matrix biases the best linear unbiased prediction.

The best linear unbiased prediction (BLUP), derived from the linear mixed model (LMM), has been popularly used to estimate animal and plant breeding values (BVs) for a few decades. Conventional BLUP has a constraint that BVs are estimated from the assumed covariance among unknown BVs, namely conventional BLUP assumes that its covariance matrix is a λK, in which λ is a coefficient that leads to the minimum mean square error of the LMM, and K is a genetic relationship matrix. The uncertainty regarding the use of λK in conventional BLUP was recognized by past studies, but it has not been sufficiently investigated. This study was motivated to answer the following question: is it indeed reasonable to use a λK in conventional BLUP? The mathematical investigation concluded: (i) the use of a λK in conventional BLUP biases the estimated BVs, and (ii) the objective BLUP, mathematically derived from the LMM, has the same representation as the least squares.

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data.

In Oryza sativa, indica and japonica are pivotal subpopulations, and other subpopulations such as aus and aromatic are considered to be derived from indica or japonica. In this regard, Oryza sativa accessions are frequently viewed from the indica/japonica perspective. This study introduces a computational method for indica/japonica classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 Oryza sativa accessions, of which 280 accessions were indica or japonica. Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate indica/japonica separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 indica/japonica accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the indica/japonica classification with phenotypic variables in Oryza sativa.

GWASpro: a high-performance genome-wide association analysis server.

SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function.

This article introduces a new method for genome-wide association study (GWAS), hierarchical hypergeometric complementary cumulative distribution function (HH-CCDF). Existing GWAS methods, e.g. the linear model and hierarchical association coefficient algorithm, calculate the association between a marker variable and a phenotypic variable. The ideal GWAS practice is to calculate the association between a marker variable and a gene-signal variable. If the gene-signal variable and phenotypic variable are imperfectly proportional, the existing methods do not properly reveal the magnitude of the association between the gene-signal variable and marker variable. The HH-CCDF mitigates the impact of the imperfect proportionality between the phenotypic variable and gene-signal variable and thus better reveals the magnitude of gene signals. The HH-CCDF will provide new insights into GWAS approaches from the viewpoint of revealing the magnitude of gene signals.

The protective role of macrophage migration inhibitory factor in acute kidney injury after cardiac surgery.

Acute kidney injury (AKI) represents the most frequent complication after cardiac surgery. Macrophage migration inhibitory factor (MIF) is a stress-regulating cytokine that was shown to protect the heart from myocardial ischemia-reperfusion injury, but its role in the pathogenesis of AKI remains unknown. In an observational study, serum and urinary MIF was quantified in 60 patients scheduled for elective conventional cardiac surgery with the use of cardiopulmonary bypass. Cardiac surgery triggered an increase in MIF serum concentrations, and patients with high circulating MIF (>median) 12 hours after surgery had a significantly reduced risk of developing AKI (relative risk reduction, 72.7%; 95% confidence interval, 12 to 91.5%; P = 0.03). Experimental AKI was induced in wild-type and Mif-/- mice by 30 min of ischemia followed by 6 or 24 hours of reperfusion, or by rhabdomyolysis. Mif-deficient mice exhibited increased tubular cell injury, increased regulated cell death (necroptosis and ferroptosis), and enhanced oxidative stress. Therapeutic administration of recombinant MIF after ischemia-reperfusion in mice ameliorated AKI. In vitro treatment of tubular epithelial cells with recombinant MIF reduced cell death and oxidative stress as measured by glutathione and thiobarbituric acid reactive substances in the setting of hypoxia. Our data provide evidence of a renoprotective role of MIF in experimental ischemia-reperfusion injury by protecting renal tubular epithelial cells, consistent with our observation that high MIF in cardiac surgery patients is associated with a reduced incidence of AKI.

Hierarchical Association Coefficient Algorithm: New Method for Genome-Wide Association Study.

Hierarchical association coefficient algorithm calculates the degree of association between observations and categories into a value named hierarchical association coefficient (HA-coefficient) between 0 for the lower limit and 1 for the upper limit. The HA-coefficient algorithm can be operated with stratified ascending categories based on the average of observations in each category. The upper limit refers to a condition where observations are increasingly ordered into the stratified ascending categories, whereas the lower limit refers to a condition where observations are decreasingly ordered into the stratified ascending categories. An HA-coefficient represents how close an observed categorization is to the upper limit, or how distant an observed categorization is from the lower limit. To demonstrate robustness and reliability, the HA-coefficient algorithm was applied to 3 different simulated data sets with the same pattern in terms of the association between observations and categories. From all simulated data sets, the same result was obtained, indicating that the HA-coefficient algorithm is robust and reliable.

Numericware i: Identical by State Matrix Calculator.

We introduce software, Numericware i, to compute identical by state (IBS) matrix based on genotypic data. Calculating an IBS matrix with a large dataset requires large computer memory and takes lengthy processing time. Numericware i addresses these challenges with 2 algorithmic methods: multithreading and forward chopping. The multithreading allows computational routines to concurrently run on multiple central processing unit (CPU) processors. The forward chopping addresses memory limitation by dividing a dataset into appropriately sized subsets. Numericware i allows calculation of the IBS matrix for a large genotypic dataset using a laptop or a desktop computer. For comparison with different software, we calculated genetic relationship matrices using Numericware i, SPAGeDi, and TASSEL with the same genotypic dataset. Numericware i calculates IBS coefficients between 0 and 2, whereas SPAGeDi and TASSEL produce different ranges of values including negative values. The Pearson correlation coefficient between the matrices from Numericware i and TASSEL was high at .9972, whereas SPAGeDi showed low correlation with Numericware i (.0505) and TASSEL (.0587). With a high-dimensional dataset of 500 entities by 10 000 000 SNPs, Numericware i spent 382 minutes using 19 CPU threads and 64 GB memory by dividing the dataset into 3 pieces, whereas SPAGeDi and TASSEL failed with the same dataset. Numericware i is freely available for Windows and Linux under CC-BY 4.0 license at https://figshare.com/s/f100f33a8857131eb2db.

Numericware N: Numerator Relationship Matrix Calculator.

We present the generalized numerator relationship matrix (GNRM) algorithm and Numericware N as a software tool for calculating the numerator relationship matrix (NRM). The GNRM algorithm aims to build the NRM based on plant pedigrees. Customary plant pedigrees have a sparse format representing multiple ancestors and offspring. Applying the existing NRM algorithm to plant pedigrees requires transforming the pedigree statements from sparse (multi-founders to offspring) to dense (bi-parents to offspring). The GNRM algorithm enables the computation of the NRM using sparse pedigrees. Because sparse pedigrees can be used, Numericware N produces smaller dimensions of the NRM, thus making computing time much faster. Moreover, Numericware N enables expansion of identical by state (IBS) matrix for scheduled pedigrees, which allows prediction of IBS matrix.